Merging Smart-seq2 and 10X Datasets - wild-type only

Gametocyte Development in Plasmodium berghei

---

1. Introduction and Aims

We have quality-controlled the 10X data and the SS2 data and now are left with the following objects:

10X 5K data - pb_sex_filtered

10X 30K data - pb_30k_sex_filtered

SS2 mutant data - ss2_mutants_final

2. Read in the data

Load/Install the Required Packages

[1] "patchwork is loaded correctly"
[1] "viridis is loaded correctly"
[1] "Seurat is loaded correctly"
[1] "cowplot is loaded correctly"
[1] "gridExtra is loaded correctly"
[1] "grid is loaded correctly"
[1] "Hmisc is loaded correctly"
[1] "reshape2 is loaded correctly"
[1] "dplyr is loaded correctly"

Loading required package: Nebulosa

[1] "Nebulosa is loaded correctly"
[1] "monocle3 is loaded correctly"

Read in the Data

screen hits

## EDIT - change this to the excel table once we have it finalized for the screen
screen_hits <- c("PBANKA-0516300",
"PBANKA-1217700",
"PBANKA-0409100",
"PBANKA-1034300",
"PBANKA-1437500",
"PBANKA-0827500",
"PBANKA-0824300",
"PBANKA-1426900",
"PBANKA-0105300",
"PBANKA-0921100",
"PBANKA-1002400",
"PBANKA-0829400",
"PBANKA-1347200",
"PBANKA-0828000",
"PBANKA-0902300",
"PBANKA-1418100",
"PBANKA-1435200",
"PBANKA-1454800",
"PBANKA-0712300",
"PBANKA-0410500",
"PBANKA-1144800",
"PBANKA-1231600",
"PBANKA-0503200",
"PBANKA-0308900",
"PBANKA-1214700",
"PBANKA-0709900",
"PBANKA-0311900",
"PBANKA-0716500",
"PBANKA-1447900",
"PBANKA-0102200",
"PBANKA-0713500",
"PBANKA-0102400",
"PBANKA-1302700",
"PBANKA-1235900",
"PBANKA-0401100",
"PBANKA-0413400",
"PBANKA-1126900",
"PBANKA-1425900",
"PBANKA-0418300",
"PBANKA-1464600",
"PBANKA-0806000")

Read in gene annotations

gene_annotations <- read.table("../data/Reference/GenesByTaxon_Summary.csv", header = TRUE, sep = ",", stringsAsFactors = TRUE)
dim(gene_annotations)

[1] 5254    7

## convert _ to -
gene_annotations$Gene.ID <- gsub("_", "-", gene_annotations$Gene.ID)

load in datasets

## load the 10X dataset
pb_sex_filtered <- readRDS("../data_to_export/pb_sex_filtered.RDS")
## load the SS2 dataset
ss2_mutants_final <- readRDS("../data_to_export/ss2_mutants_final.RDS")

## inspect
paste("10x dataset")

[1] "10x dataset"

pb_sex_filtered

An object of class Seurat 
5098 features across 6191 samples within 1 assay 
Active assay: RNA (5098 features, 2000 variable features)
 2 dimensional reductions calculated: pca, umap

paste("Smart-seq2 dataset")

[1] "Smart-seq2 dataset"

ss2_mutants_final

An object of class Seurat 
5245 features across 2717 samples within 1 assay 
Active assay: RNA (5245 features, 2000 variable features)
 2 dimensional reductions calculated: pca, umap

paste("The composition of the Smart-seq2 dataset is:")

[1] "The composition of the Smart-seq2 dataset is:"

table(ss2_mutants_final@meta.data$genotype)

Mutant     WT 
  2028    689 

3. Merging the Smart-seq2 and 10X Data

Prepare data

## extract 10x data
tenx_5k_counts <- as.matrix(pb_sex_filtered@assays$RNA@counts)
tenx_5k_pheno <- pb_sex_filtered@meta.data

## Create fresh object
tenx_5k_counts_to_integrate <- CreateSeuratObject(counts = tenx_5k_counts, meta.data = tenx_5k_pheno, min.cells = 0, min.features = 0, project = "GCSKO")

Invalid name supplied, making object name syntactically valid. New object name is orig.identnCount_RNAnFeature_RNAexperimentRNA_snn_res.1seurat_clusterspANN_0.25_0.01_440DF.classifications_0.25_0.01_440Prediction.Spearman.r.Spearman.Prediction.Pearsons.r.Pearsons.Prediction.Spearman._Kasiar.Spearman._KasiaPrediction.Pearson._Kasiar.Pearson._KasiaRNA_snn_res.2RNA_snn_res.3; see ?make.names for more details on syntax validity

## add experiment meta data
tenx_5k_counts_to_integrate@meta.data$experiment <- "tenx_5k"

## inspect
tenx_5k_counts_to_integrate

An object of class Seurat 
5098 features across 6191 samples within 1 assay 
Active assay: RNA (5098 features, 0 variable features)

We need to make sure the mutant data is compatible with the 10X data. the 10X data has fewer genes represented so we need to find the intersect of the two before integration.

## extract SS2 data 
mutant_counts_for_integration <- as.matrix(ss2_mutants_final@assays$RNA@counts)
mutant_pheno_for_integration <- ss2_mutants_final@meta.data

## change counts so the :rRNA and :tRNA are not there:
rownames(mutant_counts_for_integration) <- gsub(":ncRNA", "", gsub(":rRNA", "", gsub(":tRNA", "", rownames(mutant_counts_for_integration))))

## change the gene names so that they are - rather than _:
rownames(mutant_counts_for_integration) <- gsub("_", "-", rownames(mutant_counts_for_integration))

## calculate how many of the genes overlap - 10x does start out with 5098 vs 5245
genes_in_tenx_dataset <- intersect(rownames(tenx_5k_counts), rownames(mutant_counts_for_integration))
## print number of genes that overlap
dim(mutant_counts_for_integration)

[1] 5245 2717

## subset the mutant counts to contain only 10x genes
mutant_counts_for_integration <- mutant_counts_for_integration[which(rownames(mutant_counts_for_integration) %in% genes_in_tenx_dataset), ]
## print result of genes that overlap
dim(mutant_counts_for_integration)

[1] 5018 2717

## make Seurat object:
GCSKO_mutants <- CreateSeuratObject(counts = mutant_counts_for_integration, meta.data = mutant_pheno_for_integration, min.cells = 0, min.features = 0, project = "GCSKO")

## add experiment meta data
GCSKO_mutants@meta.data$experiment <- "mutants"

## inspect
GCSKO_mutants

An object of class Seurat 
5018 features across 2717 samples within 1 assay 
Active assay: RNA (5018 features, 0 variable features)

## double check that this is the same number of genes
## subset counts so that only genes represented in the other two objects are there:
length(intersect(rownames(tenx_5k_counts), rownames(mutant_counts_for_integration)))

[1] 5018

IMPORTANT - this next step is different to GCSKO_merge as it subsets the smart-seq2 data into wild-type only.

## subset wt on ss2 data:
ss2_wt_cells <- rownames(GCSKO_mutants@meta.data[GCSKO_mutants@meta.data$genotype == "WT", ])
GCSKO_mutants_wtonly <- subset(GCSKO_mutants, cells = ss2_wt_cells)

create list and normalise:

## make list
tenx.justwt.list <- list(tenx_5k_counts_to_integrate, GCSKO_mutants_wtonly)

## prepare data
for (i in 1:length(tenx.justwt.list)) {
    tenx.justwt.list[[i]] <- NormalizeData(tenx.justwt.list[[i]], verbose = FALSE)
    tenx.justwt.list[[i]] <- FindVariableFeatures(tenx.justwt.list[[i]], selection.method = "vst", 
        nfeatures = 2000, verbose = FALSE)
}

Integrate objects

## Find anchors
tenx.justwt.anchors <- FindIntegrationAnchors(object.list = tenx.justwt.list, dims = 1:21, verbose = FALSE)

UNRELIABLE VALUE: Future (‘future_lapply-1’) unexpectedly generated random numbers without specifying argument '[future.]seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set option 'future.rng.onMisuse' to "ignore".

## Integrate data
tenx.justwt.integrated <- IntegrateData(anchorset = tenx.justwt.anchors, dims = 1:21, verbose = FALSE, features.to.integrate = genes_in_tenx_dataset)

Adding a command log without an assay associated with it

4. Dimensionality reduction

PCA

## Make the default assay integrated
DefaultAssay(tenx.justwt.integrated) <- "integrated"

## Run the standard workflow for visualization and clustering
tenx.justwt.integrated <- ScaleData(tenx.justwt.integrated, verbose = FALSE)
tenx.justwt.integrated <- RunPCA(tenx.justwt.integrated, npcs = 30, verbose = FALSE)

## inspect PCs
ElbowPlot(tenx.justwt.integrated, ndims = 30, reduction = "pca")

Optimised UMAP

After optimisation, the following UMAP can be calculated:

## Run optimised UMAP
tenx.justwt.integrated <- RunUMAP(tenx.justwt.integrated, reduction = "pca", dims = 1:10, n.neighbors = 50, seed.use = 1234, min.dist = 0.4, repulsion.strength = 0.03, local.connectivity = 150)

The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session13:54:55 UMAP embedding parameters a = 0.7669 b = 1.223
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
13:54:55 Read 6880 rows and found 10 numeric columns
13:54:56 Using Annoy for neighbor search, n_neighbors = 50
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
13:54:56 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:54:57 Writing NN index file to temp file /var/folders/7t/kvh8b3952x9_4b3r74r1kstc000glh/T//RtmpeG990J/file7f987cfd3981
13:54:57 Searching Annoy index using 1 thread, search_k = 5000
13:55:01 Annoy recall = 100%
13:55:10 Commencing smooth kNN distance calibration using 1 thread
13:55:11 6880 smooth knn distance failures
13:55:15 Initializing from normalized Laplacian + noise
13:55:16 Commencing optimization for 500 epochs, with 480972 positive edges
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:56:49 Optimization finished

## plot
dp1 <- DimPlot(tenx.justwt.integrated, label = TRUE, repel = FALSE, pt.size = 0.05, dims = c(1,2), split.by = "experiment") + 
  ## fix the axis
  coord_fixed() #+ 

Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
Please use `as_label()` or `as_name()` instead.
This warning is displayed once per session.

  ## reverse the scale
  #scale_y_reverse()

## view
dp1

FeaturePlot(tenx.justwt.integrated, features = "PBANKA-1437500", coord.fixed = TRUE, dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("AP2G (Commitment)")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))  #+ 

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

  ## reverse the scale
  #scale_x_reverse()

5. Clustering

Generate clusters

## generate new clusters at low resolution
tenx.justwt.integrated <- FindNeighbors(tenx.justwt.integrated, dims = 1:19, reduction = "pca")

Computing nearest neighbor graph
Computing SNN

tenx.justwt.integrated <- FindClusters(tenx.justwt.integrated, resolution = 2, random.seed = 42, algorithm = 2)

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 6880
Number of edges: 265395

Running Louvain algorithm with multilevel refinement...

0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Maximum modularity in 10 random starts: 0.7631
Number of communities: 24
Elapsed time: 1 seconds

visualise clusters

DimPlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE, repel = FALSE, label = TRUE)

Make individual plots highlighting where cells in each cluster fall

[1] 24

plot

## this function writes the next bit of code for you
## put it into the console and paste the response
#ploty <- c()
#for(i in seq_along(levels(tenx.justwt.integrated@meta.data$seurat_clusters))){
#  ploty <- paste0(ploty, "list_UMAPs_by_cluster[[", i, "]]", " + ")
#}

## plot
list_UMAPs_by_cluster[[1]] + list_UMAPs_by_cluster[[2]] + list_UMAPs_by_cluster[[3]] + list_UMAPs_by_cluster[[4]] + list_UMAPs_by_cluster[[5]] + list_UMAPs_by_cluster[[6]] + list_UMAPs_by_cluster[[7]] + list_UMAPs_by_cluster[[8]] + list_UMAPs_by_cluster[[9]] + list_UMAPs_by_cluster[[10]] + list_UMAPs_by_cluster[[11]] + list_UMAPs_by_cluster[[12]] + list_UMAPs_by_cluster[[13]] + list_UMAPs_by_cluster[[14]] + list_UMAPs_by_cluster[[15]] + list_UMAPs_by_cluster[[16]] + list_UMAPs_by_cluster[[17]] + list_UMAPs_by_cluster[[18]] + list_UMAPs_by_cluster[[19]] + list_UMAPs_by_cluster[[20]] + list_UMAPs_by_cluster[[21]] + list_UMAPs_by_cluster[[22]] + list_UMAPs_by_cluster[[23]] + list_UMAPs_by_cluster[[24]]

Find Markers

# find markers for every cluster compared to all remaining cells, report only the positive ones
pb.markers <- FindAllMarkers(tenx.justwt.integrated, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)

Calculating cluster 0

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Calculating cluster 1

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Calculating cluster 2

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  |++++++++++++++++++++++++++++                      | 55% ~02s          
  |+++++++++++++++++++++++++++++                     | 56% ~02s          
  |+++++++++++++++++++++++++++++                     | 58% ~02s          
  |++++++++++++++++++++++++++++++                    | 59% ~02s          
  |+++++++++++++++++++++++++++++++                   | 60% ~02s          
  |+++++++++++++++++++++++++++++++                   | 62% ~01s          
  |++++++++++++++++++++++++++++++++                  | 63% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~01s          
  |++++++++++++++++++++++++++++++++++                | 67% ~01s          
  |++++++++++++++++++++++++++++++++++                | 68% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s  

Calculating cluster 3

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~03s          
  |++                                                | 2 % ~03s          
  |++                                                | 4 % ~03s          
  |+++                                               | 5 % ~03s          
  |++++                                              | 6 % ~03s          
  |++++                                              | 7 % ~03s          
  |+++++                                             | 9 % ~03s          
  |+++++                                             | 10% ~03s          
  |++++++                                            | 11% ~03s          
  |+++++++                                           | 12% ~03s          
  |+++++++                                           | 13% ~03s          
  |++++++++                                          | 15% ~03s          
  |++++++++                                          | 16% ~03s          
  |+++++++++                                         | 17% ~03s          
  |++++++++++                                        | 18% ~03s          
  |++++++++++                                        | 20% ~03s          
  |+++++++++++                                       | 21% ~03s          
  |+++++++++++                                       | 22% ~02s          
  |++++++++++++                                      | 23% ~02s          
  |+++++++++++++                                     | 24% ~02s          
  |+++++++++++++                                     | 26% ~02s          
  |++++++++++++++                                    | 27% ~02s          
  |+++++++++++++++                                   | 28% ~02s          
  |+++++++++++++++                                   | 29% ~02s          
  |++++++++++++++++                                  | 30% ~02s          
  |++++++++++++++++                                  | 32% ~02s          
  |+++++++++++++++++                                 | 33% ~02s          
  |++++++++++++++++++                                | 34% ~02s          
  |++++++++++++++++++                                | 35% ~02s          
  |+++++++++++++++++++                               | 37% ~02s          
  |+++++++++++++++++++                               | 38% ~02s          
  |++++++++++++++++++++                              | 39% ~02s          
  |+++++++++++++++++++++                             | 40% ~02s          
  |+++++++++++++++++++++                             | 41% ~02s          
  |++++++++++++++++++++++                            | 43% ~02s          
  |++++++++++++++++++++++                            | 44% ~02s          
  |+++++++++++++++++++++++                           | 45% ~02s          
  |++++++++++++++++++++++++                          | 46% ~02s          
  |++++++++++++++++++++++++                          | 48% ~02s          
  |+++++++++++++++++++++++++                         | 49% ~02s          
  |+++++++++++++++++++++++++                         | 50% ~02s          
  |++++++++++++++++++++++++++                        | 51% ~02s          
  |+++++++++++++++++++++++++++                       | 52% ~02s          
  |+++++++++++++++++++++++++++                       | 54% ~02s          
  |++++++++++++++++++++++++++++                      | 55% ~02s          
  |+++++++++++++++++++++++++++++                     | 56% ~01s          
  |+++++++++++++++++++++++++++++                     | 57% ~01s          
  |++++++++++++++++++++++++++++++                    | 59% ~01s          
  |++++++++++++++++++++++++++++++                    | 60% ~01s          
  |+++++++++++++++++++++++++++++++                   | 61% ~01s          
  |++++++++++++++++++++++++++++++++                  | 62% ~01s          
  |++++++++++++++++++++++++++++++++                  | 63% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~01s          
  |++++++++++++++++++++++++++++++++++                | 67% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s  

Calculating cluster 4

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~07s          
  |++                                                | 2 % ~07s          
  |++                                                | 4 % ~07s          
  |+++                                               | 5 % ~07s          
  |++++                                              | 6 % ~07s          
  |++++                                              | 7 % ~06s          
  |+++++                                             | 9 % ~06s          
  |+++++                                             | 10% ~07s          
  |++++++                                            | 11% ~07s          
  |+++++++                                           | 12% ~07s          
  |+++++++                                           | 14% ~07s          
  |++++++++                                          | 15% ~07s          
  |+++++++++                                         | 16% ~06s          
  |+++++++++                                         | 17% ~06s          
  |++++++++++                                        | 19% ~06s          
  |++++++++++                                        | 20% ~06s          
  |+++++++++++                                       | 21% ~06s          
  |++++++++++++                                      | 22% ~06s          
  |++++++++++++                                      | 23% ~06s          
  |+++++++++++++                                     | 25% ~06s          
  |+++++++++++++                                     | 26% ~06s          
  |++++++++++++++                                    | 27% ~06s          
  |+++++++++++++++                                   | 28% ~06s          
  |+++++++++++++++                                   | 30% ~06s          
  |++++++++++++++++                                  | 31% ~06s          
  |+++++++++++++++++                                 | 32% ~06s          
  |+++++++++++++++++                                 | 33% ~06s          
  |++++++++++++++++++                                | 35% ~06s          
  |++++++++++++++++++                                | 36% ~06s          
  |+++++++++++++++++++                               | 37% ~05s          
  |++++++++++++++++++++                              | 38% ~05s          
  |++++++++++++++++++++                              | 40% ~05s          
  |+++++++++++++++++++++                             | 41% ~05s          
  |+++++++++++++++++++++                             | 42% ~05s          
  |++++++++++++++++++++++                            | 43% ~05s          
  |+++++++++++++++++++++++                           | 44% ~05s          
  |+++++++++++++++++++++++                           | 46% ~05s          
  |++++++++++++++++++++++++                          | 47% ~04s          
  |+++++++++++++++++++++++++                         | 48% ~04s          
  |+++++++++++++++++++++++++                         | 49% ~04s          
  |++++++++++++++++++++++++++                        | 51% ~04s          
  |++++++++++++++++++++++++++                        | 52% ~04s          
  |+++++++++++++++++++++++++++                       | 53% ~04s          
  |++++++++++++++++++++++++++++                      | 54% ~04s          
  |++++++++++++++++++++++++++++                      | 56% ~04s          
  |+++++++++++++++++++++++++++++                     | 57% ~03s          
  |++++++++++++++++++++++++++++++                    | 58% ~03s          
  |++++++++++++++++++++++++++++++                    | 59% ~03s          
  |+++++++++++++++++++++++++++++++                   | 60% ~03s          
  |+++++++++++++++++++++++++++++++                   | 62% ~03s          
  |++++++++++++++++++++++++++++++++                  | 63% ~03s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~03s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~03s          
  |++++++++++++++++++++++++++++++++++                | 67% ~03s          
  |++++++++++++++++++++++++++++++++++                | 68% ~03s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~02s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~02s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~02s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~02s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~02s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s  

Calculating cluster 5

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~09s          
  |++                                                | 2 % ~09s          
  |++                                                | 3 % ~08s          
  |+++                                               | 4 % ~08s          
  |+++                                               | 5 % ~08s          
  |++++                                              | 6 % ~08s          
  |++++                                              | 7 % ~07s          
  |+++++                                             | 8 % ~07s          
  |+++++                                             | 9 % ~08s          
  |++++++                                            | 10% ~08s          
  |++++++                                            | 11% ~08s          
  |+++++++                                           | 12% ~08s          
  |+++++++                                           | 13% ~08s          
  |++++++++                                          | 14% ~08s          
  |++++++++                                          | 15% ~08s          
  |+++++++++                                         | 16% ~08s          
  |+++++++++                                         | 17% ~08s          
  |++++++++++                                        | 18% ~08s          
  |++++++++++                                        | 19% ~07s          
  |+++++++++++                                       | 20% ~07s          
  |+++++++++++                                       | 21% ~07s          
  |++++++++++++                                      | 22% ~07s          
  |++++++++++++                                      | 23% ~07s          
  |+++++++++++++                                     | 24% ~07s          
  |+++++++++++++                                     | 26% ~07s          
  |++++++++++++++                                    | 27% ~07s          
  |++++++++++++++                                    | 28% ~07s          
  |+++++++++++++++                                   | 29% ~07s          
  |+++++++++++++++                                   | 30% ~07s          
  |++++++++++++++++                                  | 31% ~07s          
  |++++++++++++++++                                  | 32% ~07s          
  |+++++++++++++++++                                 | 33% ~06s          
  |+++++++++++++++++                                 | 34% ~06s          
  |++++++++++++++++++                                | 35% ~06s          
  |++++++++++++++++++                                | 36% ~06s          
  |+++++++++++++++++++                               | 37% ~06s          
  |+++++++++++++++++++                               | 38% ~06s          
  |++++++++++++++++++++                              | 39% ~06s          
  |++++++++++++++++++++                              | 40% ~06s          
  |+++++++++++++++++++++                             | 41% ~05s          
  |+++++++++++++++++++++                             | 42% ~05s          
  |++++++++++++++++++++++                            | 43% ~05s          
  |++++++++++++++++++++++                            | 44% ~05s          
  |+++++++++++++++++++++++                           | 45% ~05s          
  |+++++++++++++++++++++++                           | 46% ~05s          
  |++++++++++++++++++++++++                          | 47% ~05s          
  |++++++++++++++++++++++++                          | 48% ~05s          
  |+++++++++++++++++++++++++                         | 49% ~05s          
  |+++++++++++++++++++++++++                         | 50% ~04s          
  |++++++++++++++++++++++++++                        | 51% ~04s          
  |+++++++++++++++++++++++++++                       | 52% ~04s          
  |+++++++++++++++++++++++++++                       | 53% ~04s          
  |++++++++++++++++++++++++++++                      | 54% ~04s          
  |++++++++++++++++++++++++++++                      | 55% ~04s          
  |+++++++++++++++++++++++++++++                     | 56% ~04s          
  |+++++++++++++++++++++++++++++                     | 57% ~04s          
  |++++++++++++++++++++++++++++++                    | 58% ~04s          
  |++++++++++++++++++++++++++++++                    | 59% ~04s          
  |+++++++++++++++++++++++++++++++                   | 60% ~04s          
  |+++++++++++++++++++++++++++++++                   | 61% ~04s          
  |++++++++++++++++++++++++++++++++                  | 62% ~04s          
  |++++++++++++++++++++++++++++++++                  | 63% ~03s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~03s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~03s          
  |++++++++++++++++++++++++++++++++++                | 66% ~03s          
  |++++++++++++++++++++++++++++++++++                | 67% ~03s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~03s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~03s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~03s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~03s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~03s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~02s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~02s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09s  

Calculating cluster 6

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~04s          
  |+                                                 | 2 % ~05s          
  |++                                                | 3 % ~05s          
  |++                                                | 4 % ~05s          
  |+++                                               | 5 % ~05s          
  |+++                                               | 6 % ~05s          
  |++++                                              | 7 % ~05s          
  |++++                                              | 8 % ~04s          
  |+++++                                             | 9 % ~04s          
  |+++++                                             | 10% ~04s          
  |++++++                                            | 11% ~04s          
  |++++++                                            | 12% ~04s          
  |+++++++                                           | 13% ~04s          
  |+++++++                                           | 14% ~04s          
  |++++++++                                          | 15% ~04s          
  |++++++++                                          | 16% ~04s          
  |+++++++++                                         | 17% ~04s          
  |+++++++++                                         | 18% ~04s          
  |++++++++++                                        | 19% ~04s          
  |++++++++++                                        | 20% ~04s          
  |+++++++++++                                       | 21% ~04s          
  |+++++++++++                                       | 22% ~04s          
  |++++++++++++                                      | 23% ~04s          
  |++++++++++++                                      | 24% ~04s          
  |+++++++++++++                                     | 25% ~04s          
  |+++++++++++++                                     | 26% ~04s          
  |++++++++++++++                                    | 27% ~04s          
  |++++++++++++++                                    | 28% ~04s          
  |+++++++++++++++                                   | 29% ~04s          
  |+++++++++++++++                                   | 30% ~04s          
  |++++++++++++++++                                  | 31% ~03s          
  |++++++++++++++++                                  | 32% ~03s          
  |+++++++++++++++++                                 | 33% ~03s          
  |+++++++++++++++++                                 | 34% ~03s          
  |++++++++++++++++++                                | 35% ~03s          
  |++++++++++++++++++                                | 36% ~03s          
  |+++++++++++++++++++                               | 37% ~03s          
  |+++++++++++++++++++                               | 38% ~03s          
  |++++++++++++++++++++                              | 39% ~03s          
  |++++++++++++++++++++                              | 40% ~03s          
  |+++++++++++++++++++++                             | 41% ~03s          
  |+++++++++++++++++++++                             | 42% ~03s          
  |++++++++++++++++++++++                            | 43% ~03s          
  |++++++++++++++++++++++                            | 44% ~03s          
  |+++++++++++++++++++++++                           | 45% ~03s          
  |+++++++++++++++++++++++                           | 46% ~03s          
  |++++++++++++++++++++++++                          | 47% ~03s          
  |++++++++++++++++++++++++                          | 48% ~03s          
  |+++++++++++++++++++++++++                         | 49% ~03s          
  |+++++++++++++++++++++++++                         | 50% ~03s          
  |++++++++++++++++++++++++++                        | 51% ~03s          
  |++++++++++++++++++++++++++                        | 52% ~03s          
  |+++++++++++++++++++++++++++                       | 53% ~03s          
  |+++++++++++++++++++++++++++                       | 54% ~03s          
  |++++++++++++++++++++++++++++                      | 55% ~03s          
  |++++++++++++++++++++++++++++                      | 56% ~03s          
  |+++++++++++++++++++++++++++++                     | 57% ~03s          
  |+++++++++++++++++++++++++++++                     | 58% ~03s          
  |++++++++++++++++++++++++++++++                    | 59% ~03s          
  |++++++++++++++++++++++++++++++                    | 60% ~02s          
  |+++++++++++++++++++++++++++++++                   | 61% ~02s          
  |+++++++++++++++++++++++++++++++                   | 62% ~02s          
  |++++++++++++++++++++++++++++++++                  | 63% ~02s          
  |++++++++++++++++++++++++++++++++                  | 64% ~02s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~02s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~02s          
  |++++++++++++++++++++++++++++++++++                | 67% ~02s          
  |++++++++++++++++++++++++++++++++++                | 68% ~02s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~02s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~02s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~02s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~02s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~02s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06s  

Calculating cluster 7

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~15s          
  |++                                                | 2 % ~14s          
  |++                                                | 3 % ~14s          
  |+++                                               | 4 % ~13s          
  |+++                                               | 5 % ~14s          
  |++++                                              | 6 % ~14s          
  |++++                                              | 7 % ~15s          
  |+++++                                             | 8 % ~15s          
  |+++++                                             | 9 % ~15s          
  |++++++                                            | 11% ~15s          
  |++++++                                            | 12% ~15s          
  |+++++++                                           | 13% ~15s          
  |+++++++                                           | 14% ~15s          
  |++++++++                                          | 15% ~14s          
  |++++++++                                          | 16% ~14s          
  |+++++++++                                         | 17% ~13s          
  |+++++++++                                         | 18% ~13s          
  |++++++++++                                        | 19% ~13s          
  |++++++++++                                        | 20% ~12s          
  |+++++++++++                                       | 21% ~12s          
  |++++++++++++                                      | 22% ~12s          
  |++++++++++++                                      | 23% ~12s          
  |+++++++++++++                                     | 24% ~11s          
  |+++++++++++++                                     | 25% ~11s          
  |++++++++++++++                                    | 26% ~11s          
  |++++++++++++++                                    | 27% ~11s          
  |+++++++++++++++                                   | 28% ~11s          
  |+++++++++++++++                                   | 29% ~11s          
  |++++++++++++++++                                  | 31% ~11s          
  |++++++++++++++++                                  | 32% ~10s          
  |+++++++++++++++++                                 | 33% ~10s          
  |+++++++++++++++++                                 | 34% ~10s          
  |++++++++++++++++++                                | 35% ~10s          
  |++++++++++++++++++                                | 36% ~10s          
  |+++++++++++++++++++                               | 37% ~10s          
  |+++++++++++++++++++                               | 38% ~09s          
  |++++++++++++++++++++                              | 39% ~09s          
  |++++++++++++++++++++                              | 40% ~09s          
  |+++++++++++++++++++++                             | 41% ~09s          
  |++++++++++++++++++++++                            | 42% ~09s          
  |++++++++++++++++++++++                            | 43% ~08s          
  |+++++++++++++++++++++++                           | 44% ~08s          
  |+++++++++++++++++++++++                           | 45% ~08s          
  |++++++++++++++++++++++++                          | 46% ~08s          
  |++++++++++++++++++++++++                          | 47% ~08s          
  |+++++++++++++++++++++++++                         | 48% ~08s          
  |+++++++++++++++++++++++++                         | 49% ~07s          
  |++++++++++++++++++++++++++                        | 51% ~07s          
  |++++++++++++++++++++++++++                        | 52% ~07s          
  |+++++++++++++++++++++++++++                       | 53% ~07s          
  |+++++++++++++++++++++++++++                       | 54% ~07s          
  |++++++++++++++++++++++++++++                      | 55% ~07s          
  |++++++++++++++++++++++++++++                      | 56% ~07s          
  |+++++++++++++++++++++++++++++                     | 57% ~07s          
  |+++++++++++++++++++++++++++++                     | 58% ~07s          
  |++++++++++++++++++++++++++++++                    | 59% ~07s          
  |++++++++++++++++++++++++++++++                    | 60% ~07s          
  |+++++++++++++++++++++++++++++++                   | 61% ~07s          
  |++++++++++++++++++++++++++++++++                  | 62% ~07s          
  |++++++++++++++++++++++++++++++++                  | 63% ~07s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~07s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~07s          
  |++++++++++++++++++++++++++++++++++                | 66% ~07s          
  |++++++++++++++++++++++++++++++++++                | 67% ~07s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~06s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~06s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~06s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~06s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~06s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~05s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~05s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~05s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~05s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~05s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~04s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=20s  

Calculating cluster 8

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~04s          
  |++                                                | 2 % ~05s          
  |++                                                | 3 % ~05s          
  |+++                                               | 5 % ~05s          
  |+++                                               | 6 % ~05s          
  |++++                                              | 7 % ~05s          
  |+++++                                             | 8 % ~05s          
  |+++++                                             | 9 % ~05s          
  |++++++                                            | 10% ~04s          
  |++++++                                            | 12% ~04s          
  |+++++++                                           | 13% ~04s          
  |+++++++                                           | 14% ~04s          
  |++++++++                                          | 15% ~04s          
  |+++++++++                                         | 16% ~04s          
  |+++++++++                                         | 17% ~04s          
  |++++++++++                                        | 19% ~04s          
  |++++++++++                                        | 20% ~04s          
  |+++++++++++                                       | 21% ~04s          
  |++++++++++++                                      | 22% ~04s          
  |++++++++++++                                      | 23% ~04s          
  |+++++++++++++                                     | 24% ~04s          
  |+++++++++++++                                     | 26% ~04s          
  |++++++++++++++                                    | 27% ~04s          
  |++++++++++++++                                    | 28% ~04s          
  |+++++++++++++++                                   | 29% ~04s          
  |++++++++++++++++                                  | 30% ~04s          
  |++++++++++++++++                                  | 31% ~04s          
  |+++++++++++++++++                                 | 33% ~04s          
  |+++++++++++++++++                                 | 34% ~04s          
  |++++++++++++++++++                                | 35% ~03s          
  |+++++++++++++++++++                               | 36% ~03s          
  |+++++++++++++++++++                               | 37% ~03s          
  |++++++++++++++++++++                              | 38% ~04s          
  |++++++++++++++++++++                              | 40% ~03s          
  |+++++++++++++++++++++                             | 41% ~03s          
  |+++++++++++++++++++++                             | 42% ~03s          
  |++++++++++++++++++++++                            | 43% ~03s          
  |+++++++++++++++++++++++                           | 44% ~03s          
  |+++++++++++++++++++++++                           | 45% ~03s          
  |++++++++++++++++++++++++                          | 47% ~03s          
  |++++++++++++++++++++++++                          | 48% ~03s          
  |+++++++++++++++++++++++++                         | 49% ~03s          
  |+++++++++++++++++++++++++                         | 50% ~03s          
  |++++++++++++++++++++++++++                        | 51% ~03s          
  |+++++++++++++++++++++++++++                       | 52% ~03s          
  |+++++++++++++++++++++++++++                       | 53% ~03s          
  |++++++++++++++++++++++++++++                      | 55% ~03s          
  |++++++++++++++++++++++++++++                      | 56% ~03s          
  |+++++++++++++++++++++++++++++                     | 57% ~02s          
  |++++++++++++++++++++++++++++++                    | 58% ~02s          
  |++++++++++++++++++++++++++++++                    | 59% ~02s          
  |+++++++++++++++++++++++++++++++                   | 60% ~02s          
  |+++++++++++++++++++++++++++++++                   | 62% ~02s          
  |++++++++++++++++++++++++++++++++                  | 63% ~02s          
  |++++++++++++++++++++++++++++++++                  | 64% ~02s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~02s          
  |++++++++++++++++++++++++++++++++++                | 66% ~02s          
  |++++++++++++++++++++++++++++++++++                | 67% ~02s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~02s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~02s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~02s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~02s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s  

Calculating cluster 9

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~06s          
  |++                                                | 2 % ~07s          
  |++                                                | 3 % ~07s          
  |+++                                               | 4 % ~09s          
  |+++                                               | 5 % ~09s          
  |++++                                              | 7 % ~09s          
  |++++                                              | 8 % ~09s          
  |+++++                                             | 9 % ~09s          
  |+++++                                             | 10% ~09s          
  |++++++                                            | 11% ~09s          
  |+++++++                                           | 12% ~08s          
  |+++++++                                           | 13% ~08s          
  |++++++++                                          | 14% ~08s          
  |++++++++                                          | 15% ~08s          
  |+++++++++                                         | 16% ~08s          
  |+++++++++                                         | 18% ~08s          
  |++++++++++                                        | 19% ~07s          
  |++++++++++                                        | 20% ~07s          
  |+++++++++++                                       | 21% ~07s          
  |+++++++++++                                       | 22% ~07s          
  |++++++++++++                                      | 23% ~07s          
  |+++++++++++++                                     | 24% ~07s          
  |+++++++++++++                                     | 25% ~07s          
  |++++++++++++++                                    | 26% ~06s          
  |++++++++++++++                                    | 27% ~06s          
  |+++++++++++++++                                   | 29% ~06s          
  |+++++++++++++++                                   | 30% ~06s          
  |++++++++++++++++                                  | 31% ~06s          
  |++++++++++++++++                                  | 32% ~06s          
  |+++++++++++++++++                                 | 33% ~05s          
  |++++++++++++++++++                                | 34% ~05s          
  |++++++++++++++++++                                | 35% ~05s          
  |+++++++++++++++++++                               | 36% ~05s          
  |+++++++++++++++++++                               | 37% ~05s          
  |++++++++++++++++++++                              | 38% ~05s          
  |++++++++++++++++++++                              | 40% ~05s          
  |+++++++++++++++++++++                             | 41% ~05s          
  |+++++++++++++++++++++                             | 42% ~05s          
  |++++++++++++++++++++++                            | 43% ~04s          
  |++++++++++++++++++++++                            | 44% ~04s          
  |+++++++++++++++++++++++                           | 45% ~04s          
  |++++++++++++++++++++++++                          | 46% ~04s          
  |++++++++++++++++++++++++                          | 47% ~04s          
  |+++++++++++++++++++++++++                         | 48% ~04s          
  |+++++++++++++++++++++++++                         | 49% ~04s          
  |++++++++++++++++++++++++++                        | 51% ~04s          
  |++++++++++++++++++++++++++                        | 52% ~04s          
  |+++++++++++++++++++++++++++                       | 53% ~04s          
  |+++++++++++++++++++++++++++                       | 54% ~04s          
  |++++++++++++++++++++++++++++                      | 55% ~04s          
  |+++++++++++++++++++++++++++++                     | 56% ~03s          
  |+++++++++++++++++++++++++++++                     | 57% ~03s          
  |++++++++++++++++++++++++++++++                    | 58% ~03s          
  |++++++++++++++++++++++++++++++                    | 59% ~03s          
  |+++++++++++++++++++++++++++++++                   | 60% ~03s          
  |+++++++++++++++++++++++++++++++                   | 62% ~03s          
  |++++++++++++++++++++++++++++++++                  | 63% ~03s          
  |++++++++++++++++++++++++++++++++                  | 64% ~03s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~03s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~03s          
  |++++++++++++++++++++++++++++++++++                | 67% ~03s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~03s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~03s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~02s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~02s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~02s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~02s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s  

Calculating cluster 10

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~03s          
  |++                                                | 2 % ~03s          
  |++                                                | 3 % ~03s          
  |+++                                               | 4 % ~03s          
  |+++                                               | 5 % ~03s          
  |++++                                              | 6 % ~04s          
  |++++                                              | 7 % ~03s          
  |+++++                                             | 8 % ~04s          
  |+++++                                             | 9 % ~04s          
  |++++++                                            | 10% ~04s          
  |++++++                                            | 11% ~03s          
  |+++++++                                           | 12% ~03s          
  |+++++++                                           | 13% ~03s          
  |++++++++                                          | 14% ~03s          
  |++++++++                                          | 15% ~03s          
  |+++++++++                                         | 16% ~03s          
  |+++++++++                                         | 17% ~03s          
  |++++++++++                                        | 18% ~03s          
  |++++++++++                                        | 19% ~03s          
  |+++++++++++                                       | 20% ~03s          
  |+++++++++++                                       | 21% ~03s          
  |++++++++++++                                      | 22% ~03s          
  |++++++++++++                                      | 23% ~03s          
  |+++++++++++++                                     | 24% ~03s          
  |+++++++++++++                                     | 25% ~03s          
  |++++++++++++++                                    | 26% ~03s          
  |++++++++++++++                                    | 27% ~03s          
  |+++++++++++++++                                   | 28% ~03s          
  |+++++++++++++++                                   | 29% ~03s          
  |++++++++++++++++                                  | 30% ~03s          
  |++++++++++++++++                                  | 31% ~03s          
  |+++++++++++++++++                                 | 32% ~03s          
  |+++++++++++++++++                                 | 33% ~03s          
  |++++++++++++++++++                                | 34% ~03s          
  |++++++++++++++++++                                | 35% ~03s          
  |+++++++++++++++++++                               | 36% ~03s          
  |+++++++++++++++++++                               | 37% ~03s          
  |++++++++++++++++++++                              | 38% ~03s          
  |++++++++++++++++++++                              | 39% ~03s          
  |+++++++++++++++++++++                             | 40% ~02s          
  |+++++++++++++++++++++                             | 41% ~02s          
  |++++++++++++++++++++++                            | 42% ~02s          
  |++++++++++++++++++++++                            | 43% ~02s          
  |+++++++++++++++++++++++                           | 44% ~02s          
  |+++++++++++++++++++++++                           | 45% ~02s          
  |++++++++++++++++++++++++                          | 46% ~02s          
  |++++++++++++++++++++++++                          | 47% ~02s          
  |+++++++++++++++++++++++++                         | 48% ~02s          
  |+++++++++++++++++++++++++                         | 49% ~02s          
  |++++++++++++++++++++++++++                        | 51% ~02s          
  |++++++++++++++++++++++++++                        | 52% ~02s          
  |+++++++++++++++++++++++++++                       | 53% ~02s          
  |+++++++++++++++++++++++++++                       | 54% ~02s          
  |++++++++++++++++++++++++++++                      | 55% ~02s          
  |++++++++++++++++++++++++++++                      | 56% ~02s          
  |+++++++++++++++++++++++++++++                     | 57% ~02s          
  |+++++++++++++++++++++++++++++                     | 58% ~02s          
  |++++++++++++++++++++++++++++++                    | 59% ~02s          
  |++++++++++++++++++++++++++++++                    | 60% ~02s          
  |+++++++++++++++++++++++++++++++                   | 61% ~02s          
  |+++++++++++++++++++++++++++++++                   | 62% ~02s          
  |++++++++++++++++++++++++++++++++                  | 63% ~02s          
  |++++++++++++++++++++++++++++++++                  | 64% ~02s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~02s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~02s          
  |++++++++++++++++++++++++++++++++++                | 67% ~01s          
  |++++++++++++++++++++++++++++++++++                | 68% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s  

Calculating cluster 11

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~11s          
  |++                                                | 2 % ~12s          
  |++                                                | 3 % ~11s          
  |+++                                               | 4 % ~11s          
  |+++                                               | 5 % ~10s          
  |++++                                              | 7 % ~10s          
  |++++                                              | 8 % ~11s          
  |+++++                                             | 9 % ~11s          
  |+++++                                             | 10% ~11s          
  |++++++                                            | 11% ~11s          
  |++++++                                            | 12% ~11s          
  |+++++++                                           | 13% ~11s          
  |++++++++                                          | 14% ~11s          
  |++++++++                                          | 15% ~11s          
  |+++++++++                                         | 16% ~11s          
  |+++++++++                                         | 17% ~11s          
  |++++++++++                                        | 18% ~11s          
  |++++++++++                                        | 20% ~11s          
  |+++++++++++                                       | 21% ~11s          
  |+++++++++++                                       | 22% ~11s          
  |++++++++++++                                      | 23% ~11s          
  |++++++++++++                                      | 24% ~11s          
  |+++++++++++++                                     | 25% ~11s          
  |++++++++++++++                                    | 26% ~11s          
  |++++++++++++++                                    | 27% ~11s          
  |+++++++++++++++                                   | 28% ~10s          
  |+++++++++++++++                                   | 29% ~10s          
  |++++++++++++++++                                  | 30% ~10s          
  |++++++++++++++++                                  | 32% ~10s          
  |+++++++++++++++++                                 | 33% ~10s          
  |+++++++++++++++++                                 | 34% ~10s          
  |++++++++++++++++++                                | 35% ~09s          
  |++++++++++++++++++                                | 36% ~09s          
  |+++++++++++++++++++                               | 37% ~09s          
  |++++++++++++++++++++                              | 38% ~09s          
  |++++++++++++++++++++                              | 39% ~09s          
  |+++++++++++++++++++++                             | 40% ~09s          
  |+++++++++++++++++++++                             | 41% ~08s          
  |++++++++++++++++++++++                            | 42% ~08s          
  |++++++++++++++++++++++                            | 43% ~08s          
  |+++++++++++++++++++++++                           | 45% ~08s          
  |+++++++++++++++++++++++                           | 46% ~08s          
  |++++++++++++++++++++++++                          | 47% ~07s          
  |++++++++++++++++++++++++                          | 48% ~07s          
  |+++++++++++++++++++++++++                         | 49% ~07s          
  |+++++++++++++++++++++++++                         | 50% ~07s          
  |++++++++++++++++++++++++++                        | 51% ~07s          
  |+++++++++++++++++++++++++++                       | 52% ~06s          
  |+++++++++++++++++++++++++++                       | 53% ~06s          
  |++++++++++++++++++++++++++++                      | 54% ~06s          
  |++++++++++++++++++++++++++++                      | 55% ~06s          
  |+++++++++++++++++++++++++++++                     | 57% ~06s          
  |+++++++++++++++++++++++++++++                     | 58% ~06s          
  |++++++++++++++++++++++++++++++                    | 59% ~05s          
  |++++++++++++++++++++++++++++++                    | 60% ~05s          
  |+++++++++++++++++++++++++++++++                   | 61% ~05s          
  |+++++++++++++++++++++++++++++++                   | 62% ~05s          
  |++++++++++++++++++++++++++++++++                  | 63% ~05s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~05s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~05s          
  |++++++++++++++++++++++++++++++++++                | 66% ~04s          
  |++++++++++++++++++++++++++++++++++                | 67% ~04s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~04s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~04s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~04s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~04s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~04s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~03s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~03s          
  |+++++++++++++++++++++++++++++++++++++++           | 76% ~03s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~03s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~03s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=13s  

Calculating cluster 12

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~26s          
  |++                                                | 2 % ~27s          
  |++                                                | 3 % ~27s          
  |+++                                               | 4 % ~25s          
  |+++                                               | 5 % ~23s          
  |++++                                              | 6 % ~22s          
  |++++                                              | 7 % ~21s          
  |+++++                                             | 8 % ~20s          
  |+++++                                             | 9 % ~19s          
  |++++++                                            | 10% ~19s          
  |++++++                                            | 11% ~18s          
  |+++++++                                           | 12% ~19s          
  |+++++++                                           | 13% ~19s          
  |++++++++                                          | 14% ~18s          
  |++++++++                                          | 15% ~19s          
  |+++++++++                                         | 16% ~20s          
  |+++++++++                                         | 17% ~20s          
  |++++++++++                                        | 18% ~20s          
  |++++++++++                                        | 19% ~20s          
  |+++++++++++                                       | 20% ~19s          
  |+++++++++++                                       | 21% ~19s          
  |++++++++++++                                      | 22% ~18s          
  |++++++++++++                                      | 23% ~18s          
  |+++++++++++++                                     | 24% ~18s          
  |+++++++++++++                                     | 25% ~17s          
  |++++++++++++++                                    | 26% ~17s          
  |++++++++++++++                                    | 27% ~17s          
  |+++++++++++++++                                   | 28% ~17s          
  |+++++++++++++++                                   | 29% ~17s          
  |++++++++++++++++                                  | 30% ~17s          
  |++++++++++++++++                                  | 31% ~16s          
  |+++++++++++++++++                                 | 32% ~16s          
  |+++++++++++++++++                                 | 33% ~16s          
  |++++++++++++++++++                                | 34% ~15s          
  |++++++++++++++++++                                | 35% ~15s          
  |+++++++++++++++++++                               | 36% ~15s          
  |+++++++++++++++++++                               | 37% ~14s          
  |++++++++++++++++++++                              | 38% ~14s          
  |++++++++++++++++++++                              | 39% ~14s          
  |+++++++++++++++++++++                             | 40% ~13s          
  |+++++++++++++++++++++                             | 41% ~13s          
  |++++++++++++++++++++++                            | 42% ~13s          
  |++++++++++++++++++++++                            | 43% ~13s          
  |+++++++++++++++++++++++                           | 44% ~13s          
  |+++++++++++++++++++++++                           | 45% ~12s          
  |++++++++++++++++++++++++                          | 46% ~12s          
  |++++++++++++++++++++++++                          | 47% ~12s          
  |+++++++++++++++++++++++++                         | 48% ~12s          
  |+++++++++++++++++++++++++                         | 49% ~11s          
  |++++++++++++++++++++++++++                        | 51% ~11s          
  |++++++++++++++++++++++++++                        | 52% ~11s          
  |+++++++++++++++++++++++++++                       | 53% ~11s          
  |+++++++++++++++++++++++++++                       | 54% ~10s          
  |++++++++++++++++++++++++++++                      | 55% ~10s          
  |++++++++++++++++++++++++++++                      | 56% ~10s          
  |+++++++++++++++++++++++++++++                     | 57% ~10s          
  |+++++++++++++++++++++++++++++                     | 58% ~09s          
  |++++++++++++++++++++++++++++++                    | 59% ~09s          
  |++++++++++++++++++++++++++++++                    | 60% ~09s          
  |+++++++++++++++++++++++++++++++                   | 61% ~09s          
  |+++++++++++++++++++++++++++++++                   | 62% ~09s          
  |++++++++++++++++++++++++++++++++                  | 63% ~08s          
  |++++++++++++++++++++++++++++++++                  | 64% ~08s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~08s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~08s          
  |++++++++++++++++++++++++++++++++++                | 67% ~07s          
  |++++++++++++++++++++++++++++++++++                | 68% ~07s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~07s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~07s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~07s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~06s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~06s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~06s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~06s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~05s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~05s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~05s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~05s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=22s  

Calculating cluster 13

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~14s          
  |++                                                | 2 % ~13s          
  |++                                                | 3 % ~12s          
  |+++                                               | 4 % ~12s          
  |+++                                               | 5 % ~12s          
  |++++                                              | 6 % ~13s          
  |++++                                              | 8 % ~13s          
  |+++++                                             | 9 % ~14s          
  |+++++                                             | 10% ~14s          
  |++++++                                            | 11% ~14s          
  |++++++                                            | 12% ~14s          
  |+++++++                                           | 13% ~14s          
  |+++++++                                           | 14% ~15s          
  |++++++++                                          | 15% ~14s          
  |+++++++++                                         | 16% ~14s          
  |+++++++++                                         | 17% ~14s          
  |++++++++++                                        | 18% ~13s          
  |++++++++++                                        | 19% ~13s          
  |+++++++++++                                       | 20% ~13s          
  |+++++++++++                                       | 22% ~12s          
  |++++++++++++                                      | 23% ~12s          
  |++++++++++++                                      | 24% ~12s          
  |+++++++++++++                                     | 25% ~11s          
  |+++++++++++++                                     | 26% ~11s          
  |++++++++++++++                                    | 27% ~11s          
  |++++++++++++++                                    | 28% ~11s          
  |+++++++++++++++                                   | 29% ~11s          
  |++++++++++++++++                                  | 30% ~10s          
  |++++++++++++++++                                  | 31% ~10s          
  |+++++++++++++++++                                 | 32% ~10s          
  |+++++++++++++++++                                 | 33% ~10s          
  |++++++++++++++++++                                | 34% ~10s          
  |++++++++++++++++++                                | 35% ~10s          
  |+++++++++++++++++++                               | 37% ~10s          
  |+++++++++++++++++++                               | 38% ~10s          
  |++++++++++++++++++++                              | 39% ~10s          
  |++++++++++++++++++++                              | 40% ~09s          
  |+++++++++++++++++++++                             | 41% ~09s          
  |+++++++++++++++++++++                             | 42% ~09s          
  |++++++++++++++++++++++                            | 43% ~10s          
  |+++++++++++++++++++++++                           | 44% ~10s          
  |+++++++++++++++++++++++                           | 45% ~09s          
  |++++++++++++++++++++++++                          | 46% ~09s          
  |++++++++++++++++++++++++                          | 47% ~09s          
  |+++++++++++++++++++++++++                         | 48% ~09s          
  |+++++++++++++++++++++++++                         | 49% ~09s          
  |++++++++++++++++++++++++++                        | 51% ~08s          
  |++++++++++++++++++++++++++                        | 52% ~08s          
  |+++++++++++++++++++++++++++                       | 53% ~08s          
  |+++++++++++++++++++++++++++                       | 54% ~08s          
  |++++++++++++++++++++++++++++                      | 55% ~08s          
  |++++++++++++++++++++++++++++                      | 56% ~07s          
  |+++++++++++++++++++++++++++++                     | 57% ~07s          
  |++++++++++++++++++++++++++++++                    | 58% ~07s          
  |++++++++++++++++++++++++++++++                    | 59% ~07s          
  |+++++++++++++++++++++++++++++++                   | 60% ~07s          
  |+++++++++++++++++++++++++++++++                   | 61% ~06s          
  |++++++++++++++++++++++++++++++++                  | 62% ~06s          
  |++++++++++++++++++++++++++++++++                  | 63% ~06s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~06s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~06s          
  |++++++++++++++++++++++++++++++++++                | 67% ~05s          
  |++++++++++++++++++++++++++++++++++                | 68% ~05s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~05s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~05s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~05s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~05s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~04s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~04s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~04s          
  |+++++++++++++++++++++++++++++++++++++++           | 76% ~04s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~04s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~03s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=16s  

Calculating cluster 14

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~02s          
  |++                                                | 2 % ~02s          
  |++                                                | 3 % ~03s          
  |+++                                               | 4 % ~03s          
  |+++                                               | 5 % ~03s          
  |++++                                              | 6 % ~03s          
  |++++                                              | 7 % ~03s          
  |+++++                                             | 9 % ~03s          
  |+++++                                             | 10% ~03s          
  |++++++                                            | 11% ~03s          
  |++++++                                            | 12% ~03s          
  |+++++++                                           | 13% ~03s          
  |+++++++                                           | 14% ~03s          
  |++++++++                                          | 15% ~03s          
  |++++++++                                          | 16% ~03s          
  |+++++++++                                         | 17% ~03s          
  |++++++++++                                        | 18% ~03s          
  |++++++++++                                        | 19% ~03s          
  |+++++++++++                                       | 20% ~03s          
  |+++++++++++                                       | 21% ~03s          
  |++++++++++++                                      | 22% ~03s          
  |++++++++++++                                      | 23% ~03s          
  |+++++++++++++                                     | 24% ~03s          
  |+++++++++++++                                     | 26% ~03s          
  |++++++++++++++                                    | 27% ~02s          
  |++++++++++++++                                    | 28% ~02s          
  |+++++++++++++++                                   | 29% ~02s          
  |+++++++++++++++                                   | 30% ~02s          
  |++++++++++++++++                                  | 31% ~02s          
  |++++++++++++++++                                  | 32% ~02s          
  |+++++++++++++++++                                 | 33% ~02s          
  |++++++++++++++++++                                | 34% ~02s          
  |++++++++++++++++++                                | 35% ~02s          
  |+++++++++++++++++++                               | 36% ~02s          
  |+++++++++++++++++++                               | 37% ~02s          
  |++++++++++++++++++++                              | 38% ~02s          
  |++++++++++++++++++++                              | 39% ~02s          
  |+++++++++++++++++++++                             | 40% ~02s          
  |+++++++++++++++++++++                             | 41% ~02s          
  |++++++++++++++++++++++                            | 43% ~02s          
  |++++++++++++++++++++++                            | 44% ~02s          
  |+++++++++++++++++++++++                           | 45% ~02s          
  |+++++++++++++++++++++++                           | 46% ~02s          
  |++++++++++++++++++++++++                          | 47% ~02s          
  |++++++++++++++++++++++++                          | 48% ~02s          
  |+++++++++++++++++++++++++                         | 49% ~02s          
  |+++++++++++++++++++++++++                         | 50% ~02s          
  |++++++++++++++++++++++++++                        | 51% ~02s          
  |+++++++++++++++++++++++++++                       | 52% ~02s          
  |+++++++++++++++++++++++++++                       | 53% ~02s          
  |++++++++++++++++++++++++++++                      | 54% ~02s          
  |++++++++++++++++++++++++++++                      | 55% ~02s          
  |+++++++++++++++++++++++++++++                     | 56% ~01s          
  |+++++++++++++++++++++++++++++                     | 57% ~01s          
  |++++++++++++++++++++++++++++++                    | 59% ~01s          
  |++++++++++++++++++++++++++++++                    | 60% ~01s          
  |+++++++++++++++++++++++++++++++                   | 61% ~01s          
  |+++++++++++++++++++++++++++++++                   | 62% ~01s          
  |++++++++++++++++++++++++++++++++                  | 63% ~01s          
  |++++++++++++++++++++++++++++++++                  | 64% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~01s          
  |++++++++++++++++++++++++++++++++++                | 67% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s  

Calculating cluster 15

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~04s          
  |++                                                | 2 % ~05s          
  |++                                                | 3 % ~05s          
  |+++                                               | 4 % ~05s          
  |+++                                               | 5 % ~05s          
  |++++                                              | 6 % ~05s          
  |++++                                              | 7 % ~05s          
  |+++++                                             | 8 % ~05s          
  |+++++                                             | 9 % ~05s          
  |++++++                                            | 11% ~05s          
  |++++++                                            | 12% ~05s          
  |+++++++                                           | 13% ~04s          
  |+++++++                                           | 14% ~04s          
  |++++++++                                          | 15% ~05s          
  |++++++++                                          | 16% ~05s          
  |+++++++++                                         | 17% ~05s          
  |+++++++++                                         | 18% ~04s          
  |++++++++++                                        | 19% ~04s          
  |++++++++++                                        | 20% ~04s          
  |+++++++++++                                       | 21% ~04s          
  |++++++++++++                                      | 22% ~04s          
  |++++++++++++                                      | 23% ~04s          
  |+++++++++++++                                     | 24% ~04s          
  |+++++++++++++                                     | 25% ~04s          
  |++++++++++++++                                    | 26% ~04s          
  |++++++++++++++                                    | 27% ~04s          
  |+++++++++++++++                                   | 28% ~04s          
  |+++++++++++++++                                   | 29% ~04s          
  |++++++++++++++++                                  | 31% ~04s          
  |++++++++++++++++                                  | 32% ~04s          
  |+++++++++++++++++                                 | 33% ~04s          
  |+++++++++++++++++                                 | 34% ~03s          
  |++++++++++++++++++                                | 35% ~03s          
  |++++++++++++++++++                                | 36% ~03s          
  |+++++++++++++++++++                               | 37% ~03s          
  |+++++++++++++++++++                               | 38% ~03s          
  |++++++++++++++++++++                              | 39% ~03s          
  |++++++++++++++++++++                              | 40% ~03s          
  |+++++++++++++++++++++                             | 41% ~03s          
  |++++++++++++++++++++++                            | 42% ~03s          
  |++++++++++++++++++++++                            | 43% ~03s          
  |+++++++++++++++++++++++                           | 44% ~03s          
  |+++++++++++++++++++++++                           | 45% ~03s          
  |++++++++++++++++++++++++                          | 46% ~03s          
  |++++++++++++++++++++++++                          | 47% ~03s          
  |+++++++++++++++++++++++++                         | 48% ~03s          
  |+++++++++++++++++++++++++                         | 49% ~03s          
  |++++++++++++++++++++++++++                        | 51% ~03s          
  |++++++++++++++++++++++++++                        | 52% ~03s          
  |+++++++++++++++++++++++++++                       | 53% ~03s          
  |+++++++++++++++++++++++++++                       | 54% ~03s          
  |++++++++++++++++++++++++++++                      | 55% ~02s          
  |++++++++++++++++++++++++++++                      | 56% ~02s          
  |+++++++++++++++++++++++++++++                     | 57% ~02s          
  |+++++++++++++++++++++++++++++                     | 58% ~02s          
  |++++++++++++++++++++++++++++++                    | 59% ~02s          
  |++++++++++++++++++++++++++++++                    | 60% ~02s          
  |+++++++++++++++++++++++++++++++                   | 61% ~02s          
  |++++++++++++++++++++++++++++++++                  | 62% ~02s          
  |++++++++++++++++++++++++++++++++                  | 63% ~02s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~02s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~02s          
  |++++++++++++++++++++++++++++++++++                | 66% ~02s          
  |++++++++++++++++++++++++++++++++++                | 67% ~02s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~02s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~02s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~02s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~02s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s  

Calculating cluster 16

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~11s          
  |++                                                | 2 % ~11s          
  |++                                                | 3 % ~11s          
  |+++                                               | 4 % ~10s          
  |+++                                               | 5 % ~10s          
  |++++                                              | 7 % ~10s          
  |++++                                              | 8 % ~11s          
  |+++++                                             | 9 % ~11s          
  |+++++                                             | 10% ~11s          
  |++++++                                            | 11% ~11s          
  |++++++                                            | 12% ~11s          
  |+++++++                                           | 13% ~11s          
  |++++++++                                          | 14% ~11s          
  |++++++++                                          | 15% ~11s          
  |+++++++++                                         | 16% ~11s          
  |+++++++++                                         | 17% ~11s          
  |++++++++++                                        | 18% ~11s          
  |++++++++++                                        | 20% ~11s          
  |+++++++++++                                       | 21% ~10s          
  |+++++++++++                                       | 22% ~10s          
  |++++++++++++                                      | 23% ~10s          
  |++++++++++++                                      | 24% ~10s          
  |+++++++++++++                                     | 25% ~10s          
  |++++++++++++++                                    | 26% ~09s          
  |++++++++++++++                                    | 27% ~09s          
  |+++++++++++++++                                   | 28% ~09s          
  |+++++++++++++++                                   | 29% ~09s          
  |++++++++++++++++                                  | 30% ~09s          
  |++++++++++++++++                                  | 32% ~09s          
  |+++++++++++++++++                                 | 33% ~09s          
  |+++++++++++++++++                                 | 34% ~08s          
  |++++++++++++++++++                                | 35% ~08s          
  |++++++++++++++++++                                | 36% ~08s          
  |+++++++++++++++++++                               | 37% ~08s          
  |++++++++++++++++++++                              | 38% ~08s          
  |++++++++++++++++++++                              | 39% ~08s          
  |+++++++++++++++++++++                             | 40% ~08s          
  |+++++++++++++++++++++                             | 41% ~08s          
  |++++++++++++++++++++++                            | 42% ~08s          
  |++++++++++++++++++++++                            | 43% ~08s          
  |+++++++++++++++++++++++                           | 45% ~07s          
  |+++++++++++++++++++++++                           | 46% ~07s          
  |++++++++++++++++++++++++                          | 47% ~07s          
  |++++++++++++++++++++++++                          | 48% ~07s          
  |+++++++++++++++++++++++++                         | 49% ~07s          
  |+++++++++++++++++++++++++                         | 50% ~07s          
  |++++++++++++++++++++++++++                        | 51% ~06s          
  |+++++++++++++++++++++++++++                       | 52% ~06s          
  |+++++++++++++++++++++++++++                       | 53% ~06s          
  |++++++++++++++++++++++++++++                      | 54% ~06s          
  |++++++++++++++++++++++++++++                      | 55% ~06s          
  |+++++++++++++++++++++++++++++                     | 57% ~06s          
  |+++++++++++++++++++++++++++++                     | 58% ~05s          
  |++++++++++++++++++++++++++++++                    | 59% ~05s          
  |++++++++++++++++++++++++++++++                    | 60% ~05s          
  |+++++++++++++++++++++++++++++++                   | 61% ~05s          
  |+++++++++++++++++++++++++++++++                   | 62% ~05s          
  |++++++++++++++++++++++++++++++++                  | 63% ~05s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~05s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~05s          
  |++++++++++++++++++++++++++++++++++                | 66% ~04s          
  |++++++++++++++++++++++++++++++++++                | 67% ~04s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~04s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~04s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~04s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~04s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~04s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~03s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~03s          
  |+++++++++++++++++++++++++++++++++++++++           | 76% ~03s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~03s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~03s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=13s  

Calculating cluster 17

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~13s          
  |++                                                | 2 % ~12s          
  |++                                                | 3 % ~11s          
  |+++                                               | 4 % ~11s          
  |+++                                               | 5 % ~10s          
  |++++                                              | 6 % ~11s          
  |++++                                              | 7 % ~11s          
  |+++++                                             | 9 % ~11s          
  |+++++                                             | 10% ~11s          
  |++++++                                            | 11% ~11s          
  |++++++                                            | 12% ~11s          
  |+++++++                                           | 13% ~10s          
  |+++++++                                           | 14% ~19s          
  |++++++++                                          | 15% ~18s          
  |++++++++                                          | 16% ~18s          
  |+++++++++                                         | 17% ~17s          
  |++++++++++                                        | 18% ~16s          
  |++++++++++                                        | 19% ~15s          
  |+++++++++++                                       | 20% ~15s          
  |+++++++++++                                       | 21% ~14s          
  |++++++++++++                                      | 22% ~14s          
  |++++++++++++                                      | 23% ~13s          
  |+++++++++++++                                     | 24% ~13s          
  |+++++++++++++                                     | 26% ~12s          
  |++++++++++++++                                    | 27% ~12s          
  |++++++++++++++                                    | 28% ~12s          
  |+++++++++++++++                                   | 29% ~11s          
  |+++++++++++++++                                   | 30% ~11s          
  |++++++++++++++++                                  | 31% ~11s          
  |++++++++++++++++                                  | 32% ~10s          
  |+++++++++++++++++                                 | 33% ~10s          
  |++++++++++++++++++                                | 34% ~10s          
  |++++++++++++++++++                                | 35% ~10s          
  |+++++++++++++++++++                               | 36% ~09s          
  |+++++++++++++++++++                               | 37% ~09s          
  |++++++++++++++++++++                              | 38% ~09s          
  |++++++++++++++++++++                              | 39% ~09s          
  |+++++++++++++++++++++                             | 40% ~09s          
  |+++++++++++++++++++++                             | 41% ~09s          
  |++++++++++++++++++++++                            | 43% ~09s          
  |++++++++++++++++++++++                            | 44% ~08s          
  |+++++++++++++++++++++++                           | 45% ~08s          
  |+++++++++++++++++++++++                           | 46% ~08s          
  |++++++++++++++++++++++++                          | 47% ~08s          
  |++++++++++++++++++++++++                          | 48% ~08s          
  |+++++++++++++++++++++++++                         | 49% ~07s          
  |+++++++++++++++++++++++++                         | 50% ~07s          
  |++++++++++++++++++++++++++                        | 51% ~07s          
  |+++++++++++++++++++++++++++                       | 52% ~07s          
  |+++++++++++++++++++++++++++                       | 53% ~07s          
  |++++++++++++++++++++++++++++                      | 54% ~06s          
  |++++++++++++++++++++++++++++                      | 55% ~06s          
  |+++++++++++++++++++++++++++++                     | 56% ~06s          
  |+++++++++++++++++++++++++++++                     | 57% ~06s          
  |++++++++++++++++++++++++++++++                    | 59% ~06s          
  |++++++++++++++++++++++++++++++                    | 60% ~06s          
  |+++++++++++++++++++++++++++++++                   | 61% ~05s          
  |+++++++++++++++++++++++++++++++                   | 62% ~05s          
  |++++++++++++++++++++++++++++++++                  | 63% ~05s          
  |++++++++++++++++++++++++++++++++                  | 64% ~05s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~05s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~05s          
  |++++++++++++++++++++++++++++++++++                | 67% ~04s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~04s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~04s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~04s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~04s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~04s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~04s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~03s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~03s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~03s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~03s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~03s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=12s  

Calculating cluster 18

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~16s          
  |++                                                | 2 % ~15s          
  |++                                                | 3 % ~14s          
  |+++                                               | 4 % ~14s          
  |+++                                               | 5 % ~15s          
  |++++                                              | 6 % ~16s          
  |++++                                              | 7 % ~16s          
  |+++++                                             | 8 % ~16s          
  |+++++                                             | 9 % ~16s          
  |++++++                                            | 11% ~16s          
  |++++++                                            | 12% ~16s          
  |+++++++                                           | 13% ~16s          
  |+++++++                                           | 14% ~15s          
  |++++++++                                          | 15% ~15s          
  |++++++++                                          | 16% ~15s          
  |+++++++++                                         | 17% ~14s          
  |+++++++++                                         | 18% ~14s          
  |++++++++++                                        | 19% ~14s          
  |++++++++++                                        | 20% ~13s          
  |+++++++++++                                       | 21% ~13s          
  |++++++++++++                                      | 22% ~13s          
  |++++++++++++                                      | 23% ~13s          
  |+++++++++++++                                     | 24% ~12s          
  |+++++++++++++                                     | 25% ~12s          
  |++++++++++++++                                    | 26% ~12s          
  |++++++++++++++                                    | 27% ~12s          
  |+++++++++++++++                                   | 28% ~12s          
  |+++++++++++++++                                   | 29% ~12s          
  |++++++++++++++++                                  | 31% ~12s          
  |++++++++++++++++                                  | 32% ~12s          
  |+++++++++++++++++                                 | 33% ~12s          
  |+++++++++++++++++                                 | 34% ~11s          
  |++++++++++++++++++                                | 35% ~11s          
  |++++++++++++++++++                                | 36% ~11s          
  |+++++++++++++++++++                               | 37% ~11s          
  |+++++++++++++++++++                               | 38% ~10s          
  |++++++++++++++++++++                              | 39% ~10s          
  |++++++++++++++++++++                              | 40% ~10s          
  |+++++++++++++++++++++                             | 41% ~10s          
  |++++++++++++++++++++++                            | 42% ~09s          
  |++++++++++++++++++++++                            | 43% ~09s          
  |+++++++++++++++++++++++                           | 44% ~09s          
  |+++++++++++++++++++++++                           | 45% ~09s          
  |++++++++++++++++++++++++                          | 46% ~09s          
  |++++++++++++++++++++++++                          | 47% ~09s          
  |+++++++++++++++++++++++++                         | 48% ~09s          
  |+++++++++++++++++++++++++                         | 49% ~09s          
  |++++++++++++++++++++++++++                        | 51% ~08s          
  |++++++++++++++++++++++++++                        | 52% ~08s          
  |+++++++++++++++++++++++++++                       | 53% ~08s          
  |+++++++++++++++++++++++++++                       | 54% ~08s          
  |++++++++++++++++++++++++++++                      | 55% ~08s          
  |++++++++++++++++++++++++++++                      | 56% ~08s          
  |+++++++++++++++++++++++++++++                     | 57% ~07s          
  |+++++++++++++++++++++++++++++                     | 58% ~07s          
  |++++++++++++++++++++++++++++++                    | 59% ~07s          
  |++++++++++++++++++++++++++++++                    | 60% ~07s          
  |+++++++++++++++++++++++++++++++                   | 61% ~07s          
  |++++++++++++++++++++++++++++++++                  | 62% ~06s          
  |++++++++++++++++++++++++++++++++                  | 63% ~06s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~06s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~06s          
  |++++++++++++++++++++++++++++++++++                | 66% ~06s          
  |++++++++++++++++++++++++++++++++++                | 67% ~06s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~05s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~05s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~05s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~05s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~05s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~04s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~04s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~04s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~04s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~04s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~04s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=17s  

Calculating cluster 19

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~12s          
  |++                                                | 2 % ~11s          
  |++                                                | 3 % ~11s          
  |+++                                               | 4 % ~10s          
  |+++                                               | 5 % ~10s          
  |++++                                              | 6 % ~10s          
  |++++                                              | 7 % ~10s          
  |+++++                                             | 9 % ~10s          
  |+++++                                             | 10% ~10s          
  |++++++                                            | 11% ~10s          
  |++++++                                            | 12% ~10s          
  |+++++++                                           | 13% ~10s          
  |+++++++                                           | 14% ~10s          
  |++++++++                                          | 15% ~10s          
  |++++++++                                          | 16% ~10s          
  |+++++++++                                         | 17% ~11s          
  |++++++++++                                        | 18% ~11s          
  |++++++++++                                        | 19% ~11s          
  |+++++++++++                                       | 20% ~10s          
  |+++++++++++                                       | 21% ~10s          
  |++++++++++++                                      | 22% ~10s          
  |++++++++++++                                      | 23% ~10s          
  |+++++++++++++                                     | 24% ~10s          
  |+++++++++++++                                     | 26% ~09s          
  |++++++++++++++                                    | 27% ~09s          
  |++++++++++++++                                    | 28% ~09s          
  |+++++++++++++++                                   | 29% ~09s          
  |+++++++++++++++                                   | 30% ~09s          
  |++++++++++++++++                                  | 31% ~08s          
  |++++++++++++++++                                  | 32% ~08s          
  |+++++++++++++++++                                 | 33% ~08s          
  |++++++++++++++++++                                | 34% ~08s          
  |++++++++++++++++++                                | 35% ~08s          
  |+++++++++++++++++++                               | 36% ~08s          
  |+++++++++++++++++++                               | 37% ~07s          
  |++++++++++++++++++++                              | 38% ~07s          
  |++++++++++++++++++++                              | 39% ~07s          
  |+++++++++++++++++++++                             | 40% ~07s          
  |+++++++++++++++++++++                             | 41% ~07s          
  |++++++++++++++++++++++                            | 43% ~07s          
  |++++++++++++++++++++++                            | 44% ~07s          
  |+++++++++++++++++++++++                           | 45% ~07s          
  |+++++++++++++++++++++++                           | 46% ~07s          
  |++++++++++++++++++++++++                          | 47% ~07s          
  |++++++++++++++++++++++++                          | 48% ~06s          
  |+++++++++++++++++++++++++                         | 49% ~06s          
  |+++++++++++++++++++++++++                         | 50% ~06s          
  |++++++++++++++++++++++++++                        | 51% ~06s          
  |+++++++++++++++++++++++++++                       | 52% ~06s          
  |+++++++++++++++++++++++++++                       | 53% ~06s          
  |++++++++++++++++++++++++++++                      | 54% ~06s          
  |++++++++++++++++++++++++++++                      | 55% ~05s          
  |+++++++++++++++++++++++++++++                     | 56% ~05s          
  |+++++++++++++++++++++++++++++                     | 57% ~05s          
  |++++++++++++++++++++++++++++++                    | 59% ~05s          
  |++++++++++++++++++++++++++++++                    | 60% ~05s          
  |+++++++++++++++++++++++++++++++                   | 61% ~05s          
  |+++++++++++++++++++++++++++++++                   | 62% ~05s          
  |++++++++++++++++++++++++++++++++                  | 63% ~05s          
  |++++++++++++++++++++++++++++++++                  | 64% ~04s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~04s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~04s          
  |++++++++++++++++++++++++++++++++++                | 67% ~04s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~04s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~04s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~04s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~04s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~03s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~03s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~03s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~03s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~03s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~03s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~03s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=13s  

Calculating cluster 20

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~08s          
  |++                                                | 2 % ~08s          
  |++                                                | 3 % ~08s          
  |+++                                               | 4 % ~08s          
  |+++                                               | 5 % ~07s          
  |++++                                              | 7 % ~07s          
  |++++                                              | 8 % ~07s          
  |+++++                                             | 9 % ~08s          
  |+++++                                             | 10% ~08s          
  |++++++                                            | 11% ~08s          
  |+++++++                                           | 12% ~08s          
  |+++++++                                           | 13% ~07s          
  |++++++++                                          | 14% ~07s          
  |++++++++                                          | 15% ~07s          
  |+++++++++                                         | 16% ~07s          
  |+++++++++                                         | 18% ~07s          
  |++++++++++                                        | 19% ~07s          
  |++++++++++                                        | 20% ~07s          
  |+++++++++++                                       | 21% ~07s          
  |+++++++++++                                       | 22% ~07s          
  |++++++++++++                                      | 23% ~07s          
  |+++++++++++++                                     | 24% ~07s          
  |+++++++++++++                                     | 25% ~07s          
  |++++++++++++++                                    | 26% ~07s          
  |++++++++++++++                                    | 27% ~06s          
  |+++++++++++++++                                   | 29% ~06s          
  |+++++++++++++++                                   | 30% ~06s          
  |++++++++++++++++                                  | 31% ~06s          
  |++++++++++++++++                                  | 32% ~06s          
  |+++++++++++++++++                                 | 33% ~06s          
  |++++++++++++++++++                                | 34% ~06s          
  |++++++++++++++++++                                | 35% ~06s          
  |+++++++++++++++++++                               | 36% ~06s          
  |+++++++++++++++++++                               | 37% ~06s          
  |++++++++++++++++++++                              | 38% ~05s          
  |++++++++++++++++++++                              | 40% ~05s          
  |+++++++++++++++++++++                             | 41% ~05s          
  |+++++++++++++++++++++                             | 42% ~05s          
  |++++++++++++++++++++++                            | 43% ~05s          
  |++++++++++++++++++++++                            | 44% ~05s          
  |+++++++++++++++++++++++                           | 45% ~05s          
  |++++++++++++++++++++++++                          | 46% ~05s          
  |++++++++++++++++++++++++                          | 47% ~04s          
  |+++++++++++++++++++++++++                         | 48% ~04s          
  |+++++++++++++++++++++++++                         | 49% ~04s          
  |++++++++++++++++++++++++++                        | 51% ~04s          
  |++++++++++++++++++++++++++                        | 52% ~04s          
  |+++++++++++++++++++++++++++                       | 53% ~04s          
  |+++++++++++++++++++++++++++                       | 54% ~04s          
  |++++++++++++++++++++++++++++                      | 55% ~04s          
  |+++++++++++++++++++++++++++++                     | 56% ~04s          
  |+++++++++++++++++++++++++++++                     | 57% ~04s          
  |++++++++++++++++++++++++++++++                    | 58% ~04s          
  |++++++++++++++++++++++++++++++                    | 59% ~03s          
  |+++++++++++++++++++++++++++++++                   | 60% ~03s          
  |+++++++++++++++++++++++++++++++                   | 62% ~03s          
  |++++++++++++++++++++++++++++++++                  | 63% ~03s          
  |++++++++++++++++++++++++++++++++                  | 64% ~03s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~03s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~03s          
  |++++++++++++++++++++++++++++++++++                | 67% ~03s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~03s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~03s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~03s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~03s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~02s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~02s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09s  

Calculating cluster 21

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~19s          
  |++                                                | 2 % ~17s          
  |++                                                | 3 % ~16s          
  |+++                                               | 4 % ~16s          
  |+++                                               | 5 % ~17s          
  |++++                                              | 6 % ~17s          
  |++++                                              | 7 % ~17s          
  |+++++                                             | 8 % ~17s          
  |+++++                                             | 9 % ~17s          
  |++++++                                            | 10% ~17s          
  |++++++                                            | 11% ~18s          
  |+++++++                                           | 12% ~17s          
  |+++++++                                           | 13% ~17s          
  |++++++++                                          | 14% ~16s          
  |++++++++                                          | 15% ~16s          
  |+++++++++                                         | 16% ~15s          
  |+++++++++                                         | 17% ~15s          
  |++++++++++                                        | 18% ~15s          
  |++++++++++                                        | 19% ~14s          
  |+++++++++++                                       | 20% ~14s          
  |+++++++++++                                       | 21% ~14s          
  |++++++++++++                                      | 22% ~13s          
  |++++++++++++                                      | 23% ~13s          
  |+++++++++++++                                     | 24% ~13s          
  |+++++++++++++                                     | 26% ~13s          
  |++++++++++++++                                    | 27% ~13s          
  |++++++++++++++                                    | 28% ~13s          
  |+++++++++++++++                                   | 29% ~13s          
  |+++++++++++++++                                   | 30% ~13s          
  |++++++++++++++++                                  | 31% ~13s          
  |++++++++++++++++                                  | 32% ~13s          
  |+++++++++++++++++                                 | 33% ~12s          
  |+++++++++++++++++                                 | 34% ~12s          
  |++++++++++++++++++                                | 35% ~12s          
  |++++++++++++++++++                                | 36% ~12s          
  |+++++++++++++++++++                               | 37% ~11s          
  |+++++++++++++++++++                               | 38% ~11s          
  |++++++++++++++++++++                              | 39% ~11s          
  |++++++++++++++++++++                              | 40% ~11s          
  |+++++++++++++++++++++                             | 41% ~10s          
  |+++++++++++++++++++++                             | 42% ~10s          
  |++++++++++++++++++++++                            | 43% ~10s          
  |++++++++++++++++++++++                            | 44% ~10s          
  |+++++++++++++++++++++++                           | 45% ~10s          
  |+++++++++++++++++++++++                           | 46% ~10s          
  |++++++++++++++++++++++++                          | 47% ~10s          
  |++++++++++++++++++++++++                          | 48% ~09s          
  |+++++++++++++++++++++++++                         | 49% ~09s          
  |+++++++++++++++++++++++++                         | 50% ~09s          
  |++++++++++++++++++++++++++                        | 51% ~09s          
  |+++++++++++++++++++++++++++                       | 52% ~09s          
  |+++++++++++++++++++++++++++                       | 53% ~08s          
  |++++++++++++++++++++++++++++                      | 54% ~08s          
  |++++++++++++++++++++++++++++                      | 55% ~08s          
  |+++++++++++++++++++++++++++++                     | 56% ~08s          
  |+++++++++++++++++++++++++++++                     | 57% ~07s          
  |++++++++++++++++++++++++++++++                    | 58% ~07s          
  |++++++++++++++++++++++++++++++                    | 59% ~07s          
  |+++++++++++++++++++++++++++++++                   | 60% ~07s          
  |+++++++++++++++++++++++++++++++                   | 61% ~07s          
  |++++++++++++++++++++++++++++++++                  | 62% ~07s          
  |++++++++++++++++++++++++++++++++                  | 63% ~06s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~06s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~06s          
  |++++++++++++++++++++++++++++++++++                | 66% ~06s          
  |++++++++++++++++++++++++++++++++++                | 67% ~06s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~06s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~05s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~05s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~05s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~05s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~05s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~04s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~04s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~04s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~04s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~04s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=17s  

Calculating cluster 22

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~16s          
  |++                                                | 2 % ~15s          
  |++                                                | 3 % ~15s          
  |+++                                               | 4 % ~15s          
  |+++                                               | 5 % ~15s          
  |++++                                              | 6 % ~15s          
  |++++                                              | 7 % ~16s          
  |+++++                                             | 8 % ~16s          
  |+++++                                             | 9 % ~16s          
  |++++++                                            | 10% ~16s          
  |++++++                                            | 11% ~16s          
  |+++++++                                           | 12% ~17s          
  |+++++++                                           | 13% ~16s          
  |++++++++                                          | 14% ~16s          
  |++++++++                                          | 15% ~15s          
  |+++++++++                                         | 16% ~15s          
  |+++++++++                                         | 18% ~15s          
  |++++++++++                                        | 19% ~14s          
  |++++++++++                                        | 20% ~14s          
  |+++++++++++                                       | 21% ~14s          
  |+++++++++++                                       | 22% ~13s          
  |++++++++++++                                      | 23% ~13s          
  |++++++++++++                                      | 24% ~13s          
  |+++++++++++++                                     | 25% ~13s          
  |+++++++++++++                                     | 26% ~12s          
  |++++++++++++++                                    | 27% ~12s          
  |++++++++++++++                                    | 28% ~12s          
  |+++++++++++++++                                   | 29% ~12s          
  |+++++++++++++++                                   | 30% ~12s          
  |++++++++++++++++                                  | 31% ~12s          
  |++++++++++++++++                                  | 32% ~12s          
  |+++++++++++++++++                                 | 33% ~12s          
  |++++++++++++++++++                                | 34% ~11s          
  |++++++++++++++++++                                | 35% ~11s          
  |+++++++++++++++++++                               | 36% ~11s          
  |+++++++++++++++++++                               | 37% ~11s          
  |++++++++++++++++++++                              | 38% ~11s          
  |++++++++++++++++++++                              | 39% ~10s          
  |+++++++++++++++++++++                             | 40% ~10s          
  |+++++++++++++++++++++                             | 41% ~10s          
  |++++++++++++++++++++++                            | 42% ~10s          
  |++++++++++++++++++++++                            | 43% ~10s          
  |+++++++++++++++++++++++                           | 44% ~09s          
  |+++++++++++++++++++++++                           | 45% ~09s          
  |++++++++++++++++++++++++                          | 46% ~09s          
  |++++++++++++++++++++++++                          | 47% ~09s          
  |+++++++++++++++++++++++++                         | 48% ~09s          
  |+++++++++++++++++++++++++                         | 49% ~09s          
  |++++++++++++++++++++++++++                        | 51% ~08s          
  |++++++++++++++++++++++++++                        | 52% ~08s          
  |+++++++++++++++++++++++++++                       | 53% ~08s          
  |+++++++++++++++++++++++++++                       | 54% ~08s          
  |++++++++++++++++++++++++++++                      | 55% ~08s          
  |++++++++++++++++++++++++++++                      | 56% ~08s          
  |+++++++++++++++++++++++++++++                     | 57% ~07s          
  |+++++++++++++++++++++++++++++                     | 58% ~07s          
  |++++++++++++++++++++++++++++++                    | 59% ~07s          
  |++++++++++++++++++++++++++++++                    | 60% ~07s          
  |+++++++++++++++++++++++++++++++                   | 61% ~07s          
  |+++++++++++++++++++++++++++++++                   | 62% ~06s          
  |++++++++++++++++++++++++++++++++                  | 63% ~06s          
  |++++++++++++++++++++++++++++++++                  | 64% ~06s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~06s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~06s          
  |++++++++++++++++++++++++++++++++++                | 67% ~06s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~05s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~05s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~05s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~05s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~05s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~05s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~04s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~04s          
  |+++++++++++++++++++++++++++++++++++++++           | 76% ~04s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~04s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~04s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=18s  

Calculating cluster 23

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~05s          
  |++                                                | 2 % ~06s          
  |++                                                | 3 % ~06s          
  |+++                                               | 4 % ~06s          
  |+++                                               | 5 % ~06s          
  |++++                                              | 6 % ~06s          
  |++++                                              | 7 % ~06s          
  |+++++                                             | 9 % ~06s          
  |+++++                                             | 10% ~06s          
  |++++++                                            | 11% ~06s          
  |++++++                                            | 12% ~06s          
  |+++++++                                           | 13% ~06s          
  |+++++++                                           | 14% ~06s          
  |++++++++                                          | 15% ~06s          
  |++++++++                                          | 16% ~06s          
  |+++++++++                                         | 17% ~06s          
  |++++++++++                                        | 18% ~06s          
  |++++++++++                                        | 19% ~06s          
  |+++++++++++                                       | 20% ~06s          
  |+++++++++++                                       | 21% ~06s          
  |++++++++++++                                      | 22% ~06s          
  |++++++++++++                                      | 23% ~06s          
  |+++++++++++++                                     | 24% ~06s          
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pb.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC)

markers_subset <- pb.markers %>% group_by(cluster) %>% top_n(n = 20, wt = avg_logFC)
markers_subset_annotated <- merge(markers_subset, gene_annotations,  by.x = "gene", by.y = "Gene.ID", all = FALSE)
markers_subset_annotated

View(markers_subset_annotated)

visualise markers

top10 <- pb.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)
DoHeatmap(tenx.justwt.integrated, features = top10$gene) + NoLegend()

The following features were omitted as they were not found in the scale.data slot for the integrated assay: PBANKA-0806300, PBANKA-1358600, PBANKA-1202100, PBANKA-0943100, PBANKA-0601600, PBANKA-0611800, PBANKA-1230100, PBANKA-1121700, PBANKA-0719500, PBANKA-0105300, PBANKA-1122600, PBANKA-0407700, PBANKA-0613900, PBANKA-0316300, PBANKA-0512500, PBANKA-1462800, PBANKA-1344000, PBANKA-0906300, PBANKA-1037100, PBANKA-1332900, PBANKA-0718400, PBANKA-0919600, PBANKA-0414200, PBANKA-0409400, PBANKA-0700700, PBANKA-0623100, PBANKA-1210500, PBANKA-0315900, PBANKA-1411800, PBANKA-1214000, PBANKA-1303800, PBANKA-0202000, PBANKA-1309900, PBANKA-1200096, PBANKA-1224200, PBANKA-0722600, PBANKA-1365500, PBANKA-0300900, PBANKA-1320900, PBANKA-1126200, PBANKA-1404800

6. Define Cluster Identities

Downsampling

## check number of cells in each cluster
df_clusters <- as.data.frame(table(tenx.justwt.integrated@meta.data$integrated_snn_res.2))
plot(df_clusters)

## downsample using appropriate metrics
tenx.justwt.integrated.downsampled <- subset(tenx.justwt.integrated, downsample = 100)
  
## inspect plot
FeaturePlot(tenx.justwt.integrated.downsampled, features = "PBANKA-1437500", coord.fixed = TRUE, dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("AP2G (Commitment)")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

Marker Genes

Expression - Viridis

## find a good ring marker, to see if there is a better one than the ones reported
#markers_ring <- FindMarkers(tenx.justwt.integrated, ident.1 = c("4", "5", "16", "11", "7", "3", "9", "0", "22"))
#head(markers_ring)

# PBANKA-1319500 - CCP2 - female - used in 820 line
# PBANKA-0416100 - MG1 - dynenin heavy chain - male - used in 820 line
# PBANKA-1437500 - AP2G - commitment
# PBANKA-0831000 - MSP1 - late asexual
# PBANKA-1102200 - MSP8 - early asexual (from Bozdech paper)
# PBANKA-0711900 - HSP70 - promoter used for GFP and RFP expression in the mutants
# PBANKA-1400400 - FAMB - ring marker - discovered by looking for marker genes in data
# PBANKA-0722600 - Fam-b2 - ring marker - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5113031/ 


marker_gene_plot_CCP2 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-1319500", coord.fixed = TRUE, min.cutoff = "q1", dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("CCP2 (Female)")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_MG1 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-0416100", coord.fixed = TRUE, min.cutoff = "q1", dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("MG1 (Male)")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_AP2G <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-1437500", coord.fixed = TRUE, min.cutoff = "q1", dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("AP2G (Commitment)")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_MSP1 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-0831000", coord.fixed = TRUE, min.cutoff = "q1", dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("MSP1 (Schizont)")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_MSP8 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-1102200", coord.fixed = TRUE, min.cutoff = "q1", dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("MSP8 (Asexual)")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_SBP1 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-1101300", coord.fixed = TRUE, min.cutoff = "q1", dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("SBP1 (Ring)")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_FAMB <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-0722600", coord.fixed = TRUE, min.cutoff = "q1", dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("Fam-b2 (Ring)")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_HSP70 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-0711900", coord.fixed = TRUE, min.cutoff = "q1", dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("(HSP70; Reporter)","\n", "PBANKA_0711900")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

##original label:
# labs(title = paste("(CCP2; Female)","\n", "PBANKA_1319500"))

## plot
## cowplot method
marker_gene_plot_all <- plot_grid(marker_gene_plot_FAMB, marker_gene_plot_MSP8, marker_gene_plot_MSP1, marker_gene_plot_AP2G, marker_gene_plot_CCP2, marker_gene_plot_MG1, marker_gene_plot_HSP70, nrow=3)

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marker_gene_plot_all

## patchwork method
#marker_gene_plot_FAMB + marker_gene_plot_MSP8 + marker_gene_plot_MSP1 + marker_gene_plot_AP2G + marker_gene_plot_CCP2 + marker_gene_plot_MG1 + marker_gene_plot_HSP70

Expression - grey/purple

marker_gene_ramp <- colorRampPalette(c("#D3D3D3", "#1D1564"))(50)

marker_gene_plot_CCP2 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-1319500", coord.fixed = TRUE, min.cutoff = "q1", dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("CCP2 (Female)")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=marker_gene_ramp)

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_MG1 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-0416100", coord.fixed = TRUE, min.cutoff = "q1", dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("MG1 (Male)")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=marker_gene_ramp)

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_AP2G <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-1437500", coord.fixed = TRUE, min.cutoff = "q1", dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("AP2G (Commitment)")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=marker_gene_ramp)

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_MSP1 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-0831000", coord.fixed = TRUE, min.cutoff = "q1", dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("MSP1 (Schizont)")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=marker_gene_ramp)

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_MSP8 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-1102200", coord.fixed = TRUE, min.cutoff = "q1", dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("MSP8 (Asexual)")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=marker_gene_ramp)

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_SBP1 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-1101300", coord.fixed = TRUE, min.cutoff = "q1", dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("SBP1 (Ring)")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=marker_gene_ramp)

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_FAMB <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-0722600", coord.fixed = TRUE, min.cutoff = "q1", dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("Fam-b2 (Ring)")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=marker_gene_ramp)

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_HSP70 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-0711900", coord.fixed = TRUE, min.cutoff = "q1", dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("(HSP70; Reporter)","\n", "PBANKA_0711900")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=marker_gene_ramp)

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

## plot
## cowplot method
marker_gene_plot_all <- plot_grid(marker_gene_plot_FAMB, marker_gene_plot_MSP8, marker_gene_plot_MSP1, marker_gene_plot_AP2G, marker_gene_plot_CCP2, marker_gene_plot_MG1, marker_gene_plot_HSP70, nrow=3)

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marker_gene_plot_all

useful tools for all plots

## define male and female symbol
female_symbol <- intToUtf8(9792)
male_symbol <- intToUtf8(9794)

# PBANKA-1418100        GCSKO-17  FD3   
# PBANKA-0102400         GCSKO-2  MD3 
# PBANKA-0716500        GCSKO-19  MD4 
# PBANKA-1435200        GCSKO-20  FD4 
# PBANKA-0902300        GCSKO-13  FD2
# PBANKA-0413400    GCSKO-10_820  MD5
# PBANKA-0828000         GCSKO-3  GD1
# PBANKA-1302700       GCSKO-oom  MD1 
# PBANKA-1447900        GCSKO-29  MD2
# PBANKA-1454800        GCSKO-21  FD1
# PBANKA-1144800        GCSKO-28  FD5


marker_gene_plot_17 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-1418100", coord.fixed = TRUE, min.cutoff = "q10", max.cutoff = "q95", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("fd3")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) +
  ## add sex symbols
  annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") + 
  annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_2 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-0102400", coord.fixed = TRUE, min.cutoff = "q10", max.cutoff = "q95", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("md3")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) +
  ## add sex symbols
  annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") + 
  annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_19 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-0716500", coord.fixed = TRUE, min.cutoff = "q10", max.cutoff = "q95", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("md4")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) +
  ## add sex symbols
  annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") + 
  annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_20 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-1435200", coord.fixed = TRUE, min.cutoff = "q10", max.cutoff = "q95", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("fd4")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) +
  ## add sex symbols
  annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") + 
  annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_13 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-0902300", coord.fixed = TRUE, min.cutoff = "q10", max.cutoff = "q95", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("fd2")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) +
  ## add sex symbols
  annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") + 
  annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_10 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-0413400", coord.fixed = TRUE, min.cutoff = "q10", max.cutoff = "q95", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("md5")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) +
  ## add sex symbols
  annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") + 
  annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_3 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-0828000", coord.fixed = TRUE, min.cutoff = "q10", max.cutoff = "q95", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("gd1")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) +
  ## add sex symbols
  annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") + 
  annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_oom <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-1302700", coord.fixed = TRUE, min.cutoff = "q10", max.cutoff = "q95", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("md1")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) +
  ## add sex symbols
  annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") + 
  annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_29 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-1447900", coord.fixed = TRUE, min.cutoff = "q10", max.cutoff = "q95", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("md2")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) +
  ## add sex symbols
  annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") + 
  annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_21 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-1454800", coord.fixed = TRUE, min.cutoff = "q10", max.cutoff = "q95", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("fd1")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) +
  ## add sex symbols
  annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") + 
  annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

##original label:
# labs(title = paste("(CCP2; Female)","\n", "PBANKA_1319500"))

## make composite plot
mutant_expression_composite <- wrap_plots(marker_gene_plot_17 , marker_gene_plot_2 , marker_gene_plot_19 , marker_gene_plot_20 , marker_gene_plot_13 , marker_gene_plot_10 , marker_gene_plot_3 , marker_gene_plot_oom , marker_gene_plot_29 , marker_gene_plot_21 , ncol = 4)
           
## print
mutant_expression_composite

# PBANKA-1418100        GCSKO-17  FD3   
# PBANKA-0102400         GCSKO-2  MD3 
# PBANKA-0716500        GCSKO-19  MD4 
# PBANKA-1435200        GCSKO-20  FD4 
# PBANKA-0902300        GCSKO-13  FD2
# PBANKA-0413400    GCSKO-10_820  MD5
# PBANKA-0828000         GCSKO-3  GD1
# PBANKA-1302700       GCSKO-oom  MD1 
# PBANKA-1447900        GCSKO-29  MD2
# PBANKA-1454800        GCSKO-21  FD1
# PBANKA-1144800        GCSKO-28  FD5


marker_gene_plot_17 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-1418100", coord.fixed = TRUE,  pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("fd3")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=marker_gene_ramp) +
  ## add sex symbols
  annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") + 
  annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_2 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-0102400", coord.fixed = TRUE,  pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("md3")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=marker_gene_ramp) +
  ## add sex symbols
  annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") + 
  annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_19 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-0716500", coord.fixed = TRUE,  pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("md4")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=marker_gene_ramp) +
  ## add sex symbols
  annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") + 
  annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_20 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-1435200", coord.fixed = TRUE,  pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("fd4")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=marker_gene_ramp) +
  ## add sex symbols
  annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") + 
  annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_13 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-0902300", coord.fixed = TRUE,  pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("fd2")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=marker_gene_ramp) +
  ## add sex symbols
  annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") + 
  annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_10 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-0413400", coord.fixed = TRUE,  pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("md5")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=marker_gene_ramp) +
  ## add sex symbols
  annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") + 
  annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_3 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-0828000", coord.fixed = TRUE,  pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("gd1")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=marker_gene_ramp) +
  ## add sex symbols
  annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") + 
  annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_oom <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-1302700", coord.fixed = TRUE,  pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("md1")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=marker_gene_ramp) +
  ## add sex symbols
  annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") + 
  annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_29 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-1447900", coord.fixed = TRUE,  pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("md2")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=marker_gene_ramp) +
  ## add sex symbols
  annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") + 
  annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

marker_gene_plot_21 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-1454800", coord.fixed = TRUE,  pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("fd1")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=marker_gene_ramp) +
  ## add sex symbols
  annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") + 
  annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

##original label:
# labs(title = paste("(CCP2; Female)","\n", "PBANKA_1319500"))

## make composite plot
mutant_expression_composite <- wrap_plots(marker_gene_plot_17 , marker_gene_plot_2 , marker_gene_plot_19 , marker_gene_plot_20 , marker_gene_plot_13 , marker_gene_plot_10 , marker_gene_plot_3 , marker_gene_plot_oom , marker_gene_plot_29 , marker_gene_plot_21 , ncol = 4)
           
## print
mutant_expression_composite

Density

# PBANKA-0828000         GCSKO-3  GD1

# PBANKA-1302700       GCSKO-oom  MD1 
# PBANKA-1447900        GCSKO-29  MD2
# PBANKA-0102400         GCSKO-2  MD3 
# PBANKA-0716500        GCSKO-19  MD4 
# PBANKA-0413400    GCSKO-10_820  MD5

# PBANKA-1454800        GCSKO-21  FD1
# PBANKA-0902300        GCSKO-13  FD2
# PBANKA-1418100        GCSKO-17  FD3   
# PBANKA-1435200        GCSKO-20  FD4 

markers_list <- c("PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-0413400", "PBANKA-1454800", "PBANKA-0902300", "PBANKA-1418100", "PBANKA-1435200")

list_of_density_plots_mutant_genes <- plot_density(tenx.justwt.integrated, markers_list, joint = FALSE, combine = FALSE, dims = c(1,2), pal = "plasma", method = "ks")

## make composite plot
UMAP_composite_mutant_genes <- wrap_plots(list_of_density_plots_mutant_genes[[1]] + 
                                            coord_fixed() + 
                                            theme_void() + 
                                            labs(title = paste("gd1")) + 
                                            scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + 
                                            guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
                             list_of_density_plots_mutant_genes[[2]] + 
                               coord_fixed() + theme_void() + 
                               labs(title = paste("md1")) + 
                               scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + 
                               guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
                             list_of_density_plots_mutant_genes[[3]] + coord_fixed() + theme_void() + labs(title = paste("md2")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
                             list_of_density_plots_mutant_genes[[4]] + coord_fixed() + theme_void() + labs(title = paste("md3")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)) )+ guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
                             list_of_density_plots_mutant_genes[[5]] + coord_fixed() + theme_void() + 
  labs(title = paste("md4")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
                             list_of_density_plots_mutant_genes[[6]] + coord_fixed() + theme_void() + labs(title = paste("md5")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
                             list_of_density_plots_mutant_genes[[7]] + coord_fixed() + theme_void() + labs(title = paste("fd1")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
                             list_of_density_plots_mutant_genes[[8]] + coord_fixed() + theme_void() + labs(title = paste("fd2")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
                             list_of_density_plots_mutant_genes[[9]] + coord_fixed() + theme_void() + labs(title = paste("fd3")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
                             list_of_density_plots_mutant_genes[[10]] + coord_fixed() + theme_void() + labs(title = paste("fd4")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
                             ncol = 4)

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

UMAP_composite_mutant_genes

# PBANKA-0828000         GCSKO-3  GD1

# PBANKA-1302700       GCSKO-oom  MD1 
# PBANKA-1447900        GCSKO-29  MD2
# PBANKA-0102400         GCSKO-2  MD3 
# PBANKA-0716500        GCSKO-19  MD4 
# PBANKA-0413400    GCSKO-10_820  MD5

# PBANKA-1454800        GCSKO-21  FD1
# PBANKA-0902300        GCSKO-13  FD2
# PBANKA-1418100        GCSKO-17  FD3   
# PBANKA-1435200        GCSKO-20  FD4 

markers_list <- c("PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-0413400", "PBANKA-1454800", "PBANKA-0902300", "PBANKA-1418100", "PBANKA-1435200")

list_of_density_plots_mutant_genes <- plot_density(tenx.justwt.integrated, markers_list, joint = FALSE, combine = FALSE, dims = c(1,2), pal = "plasma", method = "ks")

## make composite plot
UMAP_composite_mutant_genes <- wrap_plots(list_of_density_plots_mutant_genes[[1]] + 
                                            coord_fixed() + 
                                            theme_void() + 
                                            labs(title = paste("gd1")) + 
                                            scale_colour_gradientn(colours=marker_gene_ramp) + 
                                            guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
                             list_of_density_plots_mutant_genes[[2]] + 
                               coord_fixed() + theme_void() + 
                               labs(title = paste("md1")) + 
                               scale_colour_gradientn(colours=marker_gene_ramp) + 
                               guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
                             list_of_density_plots_mutant_genes[[3]] + 
                               coord_fixed() + 
                               theme_void() + 
                               labs(title = paste("md2")) + scale_colour_gradientn(colours=marker_gene_ramp) + 
                               guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
                             list_of_density_plots_mutant_genes[[4]] + coord_fixed() + theme_void() + labs(title = paste("md3")) + scale_colour_gradientn(colours=marker_gene_ramp )+ guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
                             list_of_density_plots_mutant_genes[[5]] + coord_fixed() + theme_void() + 
  labs(title = paste("md4")) + scale_colour_gradientn(colours=marker_gene_ramp) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
                             list_of_density_plots_mutant_genes[[6]] + coord_fixed() + theme_void() + labs(title = paste("md5")) + scale_colour_gradientn(colours=marker_gene_ramp) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
                             list_of_density_plots_mutant_genes[[7]] + coord_fixed() + theme_void() + labs(title = paste("fd1")) + scale_colour_gradientn(colours=marker_gene_ramp) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
                             list_of_density_plots_mutant_genes[[8]] + coord_fixed() + theme_void() + labs(title = paste("fd2")) + scale_colour_gradientn(colours=marker_gene_ramp) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
                             list_of_density_plots_mutant_genes[[9]] + coord_fixed() + theme_void() + labs(title = paste("fd3")) + scale_colour_gradientn(colours=marker_gene_ramp) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
                             list_of_density_plots_mutant_genes[[10]] + coord_fixed() + theme_void() + labs(title = paste("fd4")) + scale_colour_gradientn(colours=marker_gene_ramp) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
                             ncol = 4)

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

UMAP_composite_mutant_genes

7. Pseudotime on all cells

Pseudotime calculation

## extract data from Seurat
seurat.object.all <- tenx.justwt.integrated
# counts
data <- as(as.matrix(GetAssayData(seurat.object.all, assay = "integrated", slot = "data")), 'sparseMatrix')
# meta data
pd <- data.frame(seurat.object.all@meta.data)

## keep only the columns that are relevant
#pData <- pd %>% select(orig.ident, nCount_RNA, nFeature_RNA)
## add gene short name
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))

## Construct monocle cds
monocle.object.all <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)
## preprocess
monocle.object.all = preprocess_cds(monocle.object.all, num_dim = 100, norm_method = "none")
## plot variance explained plot
#plot_pc_variance_explained(monocle.object.all)
## make monocle UMAP
#monocle.object.all = reduce_dimension(monocle.object.all, reduction_method = "UMAP", preprocess_method = "PCA", umap.metric = "euclidean", umap.n_neighbors = 20, umap.min_dist = 0.5, verbose = FALSE)
#plot_cells(monocle.object.all)

## add UMAP from Seurat
monocle.object.all@int_colData@listData$reducedDims@listData[["UMAP"]] <-seurat.object.all@reductions[["umap"]]@cell.embeddings 
plot_cells(monocle.object.all)

No trajectory to plot. Has learn_graph() been called yet?
cluster not found in colData(cds), cells will not be colored
cluster_cells() has not been called yet, can't color cells by cluster

## cluster
monocle.object.all = cluster_cells(monocle.object.all)

## plot clusters
plot_cells(monocle.object.all, color_cells_by="partition", group_cells_by="partition",  x = 1, y = 2)

No trajectory to plot. Has learn_graph() been called yet?

## reduce partitions to 1
monocle.object.all@clusters$UMAP$partitions[monocle.object.all@clusters$UMAP$partitions == "2"] <- "1"

#map pseudotime
monocle.object.all = learn_graph(monocle.object.all, learn_graph_control=list(ncenter=550, minimal_branch_len = 15), use_partition = FALSE)

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plot_cells(monocle.object.all, color_cells_by="partition", group_cells_by="partition",  x = 1, y = 2)

## a helper function to identify the root principal points:
## make cluster 2 the root
# get_earliest_principal_node <- function(cds, time_bin="7"){
#   cell_ids <- which(colData(cds)[, "seurat_clusters"] == time_bin)
#   closest_vertex <-
#   cds@principal_graph_aux[["UMAP"]]$pr_graph_cell_proj_closest_vertex
#   closest_vertex <- as.matrix(closest_vertex[colnames(cds), ])
#   root_pr_nodes <-
#   igraph::V(principal_graph(cds)[["UMAP"]])$name[as.numeric(names
#   (which.max(table(closest_vertex[cell_ids,]))))]
#   
#   root_pr_nodes
# }

## calculate pseudotime
#monocle.object.all = order_cells(monocle.object.all, root_pr_nodes=get_earliest_principal_node(monocle.object.all))
monocle.object.all = order_cells(monocle.object.all)

Listening on http://127.0.0.1:7415

## used 5 points at the beginning


## plot
umap_pt <- plot_cells(monocle.object.all, color_cells_by = "pseudotime", label_cell_groups=FALSE, cell_size = 1, x = 1, y = 2, label_branch_points=FALSE, label_leaves=FALSE, label_groups_by_cluster=FALSE, label_roots = FALSE) +
  coord_fixed() +
  theme_void() +
  labs(title = "") +
  theme(plot.title = element_text(hjust = 0.5, size=20), legend.position="bottom", legend.title=element_text (size=20), legend.text=element_text(size=20)) + 
  guides(colour = guide_colourbar(barwidth = 10, barheight = 2, title = "Pseudotime"))

## view plot
umap_pt

## help was obtained from here
## https://github.com/satijalab/seurat/issues/1658

save

ggsave("../images_to_export/UMAP_pt_wt.png", plot = umap_pt, device = "png", path = NULL, scale = 1, width = 20, height = 20, units = "cm", dpi = 300, limitsize = TRUE)

gganimnate GIF of pseuodtime

#install.packages("gganimate")
library(gganimate)
#install.packages("gifski")
#install.packages("av")
#library(gifski)
#library(av)

## make dataframe for plotting
## extract data for GGplot version of this
df_animation <- as.data.frame(monocle.object.all@int_colData@listData$reducedDims@listData[["UMAP"]])
## add pt to this data frame:
pt_values <- as.data.frame(pseudotime(monocle.object.all, reduction_method = "UMAP"))
df_animation <- merge(df_animation, pt_values, by="row.names") 
rownames(df_animation) <- df_animation$Row.names
colnames(df_animation)[4] <- "pt"

## make the static plot
p <- ggplot(df_animation, aes(x = UMAP_1, y = UMAP_2, colour = pt)) +
  geom_point() +
  scale_colour_viridis_c(option = "plasma") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "none")
## view plot
plot(p)

## make animated plot
## make a category for animation
#df_animation$group <- cut(df_animation$pt, 15)

anim <- p +
  transition_time(pt) +
  shadow_mark()

animate(anim, height = 3, width = 3, units = "in", res = 150, bg = 'transparent')

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Finalizing encoding... done!

## to change the resolution - https://stackoverflow.com/questions/49058567/define-size-for-gif-created-by-gganimate-change-dimension-resolution 

Save animation

anim_save("animated_UMAP_transparent_bg_wt.gif", path = "../images_to_export/")

## extract pt values
pt_values <- as.data.frame(pseudotime(monocle.object.all, reduction_method = "UMAP"))

tenx.justwt.integrated <- AddMetaData(tenx.justwt.integrated, pt_values, "old_pt_values")

Define cell identities with branches

Define identities of cells

male

monocle.object_male <- choose_graph_segments(monocle.object.all)

female

monocle.object_female <- choose_graph_segments(monocle.object.all)

bipotential

monocle.object_bipot <- choose_graph_segments(monocle.object.all)

asexual (pre-branch)

monocle.object_asex_pre <- choose_graph_segments(monocle.object.all)

asexual fate

monocle.object_asex_fate <- choose_graph_segments(monocle.object.all)

check

df_freq <- data.frame(table(c(colnames(monocle.object_male), colnames(monocle.object_female), colnames(monocle.object_bipot), colnames(monocle.object_asex_pre), colnames(monocle.object_asex_fate))))
paste("number of cells in seurat object is", length(colnames(monocle.object.all)), ". The number of cells selected here with an identitity is", dim(df_freq)[1])

[1] "number of cells in seurat object is 6880 . The number of cells selected here with an identitity is 6880"

df_freq <- df_freq[df_freq$Freq > 1, ]
df_freq

Inspect where these missing cells are:

# '%ni%' <- Negate('%in%')
# 
# not_assigned_cells <- colnames(monocle.object.all)[colnames(monocle.object.all) %ni% c(colnames(monocle.object_male), colnames(monocle.object_female), colnames(monocle.object_bipot), colnames(monocle.object_asex), colnames(monocle.object_asex_fate))]
# 
# DimPlot(seurat.object, repel = TRUE, label.size = 5, pt.size = 0.5, cells.highlight = not_assigned_cells, dims = c(2,1), reduction = "DIM_UMAP") +
#   coord_fixed() + 
#   scale_color_manual(values=c("#000000", "#f54e1e"))

## create annotation dataframe from these results:
df_monocle_sexes <- rbind(data.frame("cell_name" = colnames(monocle.object_male), "sex" = rep("Male", length(colnames(monocle.object_male)))),
                          data.frame("cell_name" = colnames(monocle.object_female), "sex" = rep("Female", length(colnames(monocle.object_female)))),
                          data.frame("cell_name" = colnames(monocle.object_bipot), "sex" = rep("Bipotential", length(colnames(monocle.object_bipot)))),
                          data.frame("cell_name" = colnames(monocle.object_asex_pre), "sex" = rep("Asexual_Early", length(colnames(monocle.object_asex_pre)))),
                          data.frame("cell_name" = colnames(monocle.object_asex_fate), "sex" = rep("Asexual_Late", length(colnames(monocle.object_asex_fate))))
                          #data.frame("cell_name" = not_assigned_cells, "sex" = rep("Unassigned", length(not_assigned_cells)))
                          )

dim(df_monocle_sexes)

[1] 6880    2

## order like the metadata
df_monocle_sexes <- df_monocle_sexes[match(rownames(monocle.object.all@colData), df_monocle_sexes$cell_name), ]

## add this back into the monocle object
monocle.object.all@colData$Sexes_monocle <- df_monocle_sexes$sex

## add this to the seurat object
rownames(df_monocle_sexes) <- df_monocle_sexes$cell_name
df_monocle_sexes_to_add_to_seurat <- df_monocle_sexes[,c("sex"), drop = FALSE]
tenx.justwt.integrated <- AddMetaData(tenx.justwt.integrated, df_monocle_sexes_to_add_to_seurat, col.name = "monocle_sex")

8. Plots

make composite pseudotime/ID figure

# 1 = blue - "#0052c5"
# 2 = red - "#a52b1e"
# 3 = green - "#016c00"
# 4 = yellow - "#ffe400"
#pal_sex <- c("#0052c5","#ffe400", "#a52b1e", "#016c00")

## extract pseodtime numbers and identity of cells to a dataframe
df_pt_id <- tenx.justwt.integrated@meta.data[,c("old_pt_values", "monocle_sex")]

## inspect possible values
list_of_sexes <- names(table(df_pt_id$monocle_sex))

## make a new column
df_pt_id$colour <- NA

## make colour ramps
asex_ramp <- colorRampPalette(c("#D5E3F5", "#0052c5"))
male_ramp <- colorRampPalette(c("white", "yellow", "#016c00"))
female_ramp <- colorRampPalette(c("yellow", "#a52b1e"))
bipot_ramp <- colorRampPalette(c("white", "#ffe400"))

## re-classify the cells that are unassigned cells removed from sexual branch above:
#df_pt_id[which(rownames(df_pt_id) %in% remove_cells), ]$monocle_sex <- "Asexual"

## assign values to each cluster
## help here: https://stackoverflow.com/questions/9946630/colour-points-in-a-plot-differently-depending-on-a-vector-of-values 
df_pt_id[df_pt_id$monocle_sex == "Asexual_Early" | df_pt_id$monocle_sex == "Asexual_Late", ]$colour <- asex_ramp(100)[as.numeric(cut(df_pt_id[df_pt_id$monocle_sex == "Asexual_Early" | df_pt_id$monocle_sex == "Asexual_Late", ]$old_pt_values,breaks = 100))]

df_pt_id[df_pt_id$monocle_sex == "Male", ]$colour <- male_ramp(100)[as.numeric(cut(df_pt_id[df_pt_id$monocle_sex == "Male", ]$old_pt_values,breaks = 100))]

df_pt_id[df_pt_id$monocle_sex == "Female", ]$colour <- female_ramp(100)[as.numeric(cut(df_pt_id[df_pt_id$monocle_sex == "Female", ]$old_pt_values,breaks = 100))]

df_pt_id[df_pt_id$monocle_sex == "Bipotential", ]$colour <- bipot_ramp(100)[as.numeric(cut(df_pt_id[df_pt_id$monocle_sex == "Bipotential", ]$old_pt_values,breaks = 100))]

## check everything has a value
#table(is.na(df_pt_id$colour))

## make into a df
#df_pt_id <- df_pt_id[ ,"colour", drop = FALSE]

## add back to seurat object
df <- df_pt_id[ ,"colour", drop = FALSE]
tenx.justwt.integrated <- AddMetaData(tenx.justwt.integrated, df, "pt_id_cols")
rm(df)

## plot
## extract UMAP coords
df_umap_plot <- tenx.justwt.integrated@reductions[["umap"]]@cell.embeddings
df_umap_plot <- merge(df_umap_plot, df_pt_id, by=0, all=TRUE)

## add tree
##The tree for monocle is located here:
# monocle.object@principal_graph_aux[["UMAP"]]$dp_mst 
ica_space_df <- t(monocle.object.all@principal_graph_aux[["UMAP"]]$dp_mst) %>%
      as.data.frame() %>%
      dplyr::select_(prin_graph_dim_1 = "UMAP_1", prin_graph_dim_2 = "UMAP_2") %>%
      dplyr::mutate(sample_name = rownames(.),
                    sample_state = rownames(.))

dp_mst <- monocle.object.all@principal_graph[["UMAP"]]

edge_df <- dp_mst %>%
      igraph::as_data_frame() %>%
      dplyr::select_(source = "from", target = "to") %>%
      dplyr::left_join(ica_space_df %>%
                         dplyr::select_(
                           source="sample_name",
                           source_prin_graph_dim_1="prin_graph_dim_1",
                           source_prin_graph_dim_2="prin_graph_dim_2"),
                       by = "source") %>%
      dplyr::left_join(ica_space_df %>%
                         dplyr::select_(
                           target="sample_name",
                           target_prin_graph_dim_1="prin_graph_dim_1",
                           target_prin_graph_dim_2="prin_graph_dim_2"),
                       by = "target")

## make ggplot
umap_id_pt <- ggplot(df_umap_plot, aes(x = UMAP_1, y = UMAP_2)) + 
                     geom_point(col = df_umap_plot$colour) +
                     theme_void() +
                     coord_fixed() +
                     geom_segment(aes_string(x="source_prin_graph_dim_1",
                                     y="source_prin_graph_dim_2",
                                     xend="target_prin_graph_dim_1",
                                     yend="target_prin_graph_dim_2"),
                          data=edge_df)

umap_id_pt

9. Subset sexual cells

Make a subsetted Seurat object of sexual cells.

Include the pre-branch too as well as any weird clusters that may have clustered out.

it’s been a while since we looked at the clusters so let’s check them out again:

## Plot
DimPlot(tenx.justwt.integrated, label = TRUE, repel = FALSE, pt.size = 0.05, group.by = "seurat_clusters", dims = c(1,2), reduction = "umap") + coord_fixed()

## plot
list_UMAPs_by_cluster[[1]] + list_UMAPs_by_cluster[[2]] + list_UMAPs_by_cluster[[3]] + list_UMAPs_by_cluster[[4]] + list_UMAPs_by_cluster[[5]] + list_UMAPs_by_cluster[[6]] + list_UMAPs_by_cluster[[7]] + list_UMAPs_by_cluster[[8]] + list_UMAPs_by_cluster[[9]] + list_UMAPs_by_cluster[[10]] + list_UMAPs_by_cluster[[11]] + list_UMAPs_by_cluster[[12]] + list_UMAPs_by_cluster[[13]] + list_UMAPs_by_cluster[[14]] + list_UMAPs_by_cluster[[15]] + list_UMAPs_by_cluster[[16]] + list_UMAPs_by_cluster[[17]] + list_UMAPs_by_cluster[[18]] + list_UMAPs_by_cluster[[19]] + list_UMAPs_by_cluster[[20]] + list_UMAPs_by_cluster[[21]] + list_UMAPs_by_cluster[[22]] + list_UMAPs_by_cluster[[23]] + list_UMAPs_by_cluster[[24]]

Define cells and subset

asexual_early_clusters <- c(9, 4, 15, 8, 1, 14, 2, 10, 3, 0, 6, 5)
asexual_late_clusters <- c(7, 12, 18, 20, 23)
bipotentional_early_clusters <- # 0?
bipotential_clusters <- c(11) 
male_clusters <- c(16, 13)
female_clusters <- c(21, 22, 17, 19)

## define cells
cell_names_subset_monocle_ids <- rownames(tenx.justwt.integrated@meta.data[tenx.justwt.integrated@meta.data$monocle_sex %in% c("Asexual_Early", "Bipotential", "Male", "Female"), ])

## 3, 0, 6, 5 are early clusters of asexuals before the branch
cell_names_subset_cluster_ids <- rownames(tenx.justwt.integrated@meta.data[tenx.justwt.integrated@meta.data$seurat_clusters %in% c(male_clusters, female_clusters, bipotential_clusters, 3, 0, 6, 5), ])

cell_names_subset_intersect <- intersect(cell_names_subset_monocle_ids, cell_names_subset_cluster_ids)

## subset cells into new object
tenx.justwt.integrated.sex <- subset(tenx.justwt.integrated, cells = cell_names_subset_intersect)

inspect/check

## inspect object
tenx.justwt.integrated.sex

An object of class Seurat 
10116 features across 2818 samples within 2 assays 
Active assay: integrated (5018 features, 2000 variable features)
 1 other assay present: RNA
 2 dimensional reductions calculated: pca, umap

## look at original UMAP
DimPlot(tenx.justwt.integrated.sex, label = TRUE, repel = TRUE, pt.size = 0.1, split.by = "experiment", dims = c(1,2), reduction = "umap") + coord_fixed()

Remove contaminant asexual cells

we want to remove:

## look at original UMAP
plot_sexual_subsetting <- DimPlot(tenx.justwt.integrated.sex, label = TRUE, repel = TRUE, pt.size = 0.1, dims = c(1,2), reduction = "umap") + 
  coord_fixed() + 
  geom_vline(aes(xintercept = -1, alpha = 5))

plot_sexual_subsetting

## extract cell embeddings
df_sex_cell_embeddings <- as.data.frame(tenx.justwt.integrated.sex@reductions[["umap"]]@cell.embeddings)

## subset anything lower than -0.8 in UMAP 2 and -0.1 in UMAP 1
remove_cells <- row.names(df_sex_cell_embeddings[which(df_sex_cell_embeddings$UMAP_1 < -1), ])

## plot these cells
DimPlot(tenx.justwt.integrated.sex, label = FALSE, repel = TRUE, pt.size = 0.1, cells.highlight = remove_cells, dims = c(1,2), reduction = "umap") + 
  coord_fixed() + 
  scale_color_manual(values=c("#000000", "#f54e1e")) + 
  theme_void() + 
  labs(title = paste("cells highlighted will be removed")) + 
  theme(plot.title = element_text(hjust = 0.5), legend.position = "none")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

Final Subset

## make keep cells from the remove_cells
## make the not in function
'%ni%' <- Negate('%in%')
keep_cells <- colnames(tenx.justwt.integrated.sex)[which(colnames(tenx.justwt.integrated.sex) %ni% remove_cells)]

## subset
tenx.justwt.integrated.sex <- subset(tenx.justwt.integrated.sex, cells = keep_cells)

## inspect
tenx.justwt.integrated.sex

An object of class Seurat 
10116 features across 2817 samples within 2 assays 
Active assay: integrated (5018 features, 2000 variable features)
 1 other assay present: RNA
 2 dimensional reductions calculated: pca, umap

copy old clusters over

## copy old clusters
tenx.justwt.integrated.sex <- AddMetaData(tenx.justwt.integrated.sex, tenx.justwt.integrated.sex@meta.data$seurat_clusters, col.name = "post_integration_clusters")

10. Save and Export

Save environment

## This saves everything in the global environment for easy recall later
#save.image(file = "GCSKO_merge.RData")
#load(file = "GCSKO_merge.RData")

Save object(s)

saveRDS(tenx.justwt.integrated, file = "../data_to_export/tenx.justwt.integrated.RDS") 

Error in saveRDS(tenx.justwt.integrated, file = "../data_to_export/tenx.justwt.integrated.RDS") : 
  object 'tenx.justwt.integrated' not found

Clean up

#rm(ss2_wt_cells)
#rm(tenx.justwt.integrated)
#rm(tenx.justwt.list)

Appendix

Session Info

R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
 [1] stats4    parallel  grid      stats     graphics  grDevices utils     datasets  methods  
[10] base     

other attached packages:
 [1] gganimate_1.0.7             shiny_1.5.0                 Nebulosa_1.2.0             
 [4] destiny_3.2.0               monocle3_0.2.3.0            SingleCellExperiment_1.10.1
 [7] SummarizedExperiment_1.18.2 DelayedArray_0.14.1         matrixStats_0.57.0         
[10] GenomicRanges_1.40.0        GenomeInfoDb_1.24.2         IRanges_2.22.2             
[13] S4Vectors_0.26.1            Biobase_2.48.0              BiocGenerics_0.34.0        
[16] circlize_0.4.12             dplyr_1.0.2                 reshape2_1.4.4             
[19] Hmisc_4.4-1                 ggplot2_3.3.2               Formula_1.2-4              
[22] survival_3.2-7              lattice_0.20-41             gridExtra_2.3              
[25] cowplot_1.1.0               Seurat_3.2.2                viridis_0.5.1              
[28] viridisLite_0.3.0           patchwork_1.0.1            

loaded via a namespace (and not attached):
  [1] ggthemes_4.2.4            tidyr_1.1.2               knitr_1.30               
  [4] irlba_2.3.3               data.table_1.13.2         rpart_4.1-15             
  [7] RCurl_1.98-1.2            generics_0.0.2            leidenbase_0.1.2         
 [10] callr_3.5.1               usethis_1.6.3             RANN_2.6.1               
 [13] proxy_0.4-24              future_1.19.1             spatstat.data_1.4-3      
 [16] httpuv_1.5.4              assertthat_0.2.1          gifski_0.8.6             
 [19] xfun_0.18                 hms_0.5.3                 evaluate_0.14            
 [22] promises_1.1.1            DEoptimR_1.0-8            fansi_0.4.1              
 [25] progress_1.2.2            readxl_1.3.1              igraph_1.2.6             
 [28] htmlwidgets_1.5.2         purrr_0.3.4               ellipsis_0.3.1           
 [31] RSpectra_0.16-0           ks_1.12.0                 backports_1.1.10         
 [34] deldir_0.1-29             vctrs_0.3.4               remotes_2.2.0            
 [37] TTR_0.24.2                ROCR_1.0-11               abind_1.4-5              
 [40] RcppEigen_0.3.3.7.0       withr_2.3.0               grr_0.9.5                
 [43] robustbase_0.93-7         checkmate_2.0.0           vcd_1.4-8                
 [46] sctransform_0.3.1         xts_0.12.1                prettyunits_1.1.1        
 [49] mclust_5.4.7              goftest_1.2-2             cluster_2.1.0            
 [52] dotCall64_1.0-0           lazyeval_0.2.2            laeken_0.5.1             
 [55] crayon_1.3.4              labeling_0.4.2            pkgconfig_2.0.3          
 [58] tweenr_1.0.1              nlme_3.1-149              pkgload_1.1.0            
 [61] nnet_7.3-14               devtools_2.3.2            rlang_0.4.8              
 [64] globals_0.13.1            lifecycle_0.2.0           miniUI_0.1.1.1           
 [67] rsvd_1.0.3                cellranger_1.1.0          rprojroot_1.3-2          
 [70] polyclip_1.10-0           RcppHNSW_0.3.0            lmtest_0.9-38            
 [73] Matrix_1.2-18             raster_3.3-13             carData_3.0-4            
 [76] boot_1.3-25               zoo_1.8-8                 Matrix.utils_0.9.8       
 [79] base64enc_0.1-3           ggridges_0.5.2            GlobalOptions_0.1.2      
 [82] processx_3.4.4            pheatmap_1.0.12           png_0.1-7                
 [85] bitops_1.0-6              KernSmooth_2.23-17        spam_2.5-1               
 [88] DelayedMatrixStats_1.10.1 shape_1.4.5               stringr_1.4.0            
 [91] jpeg_0.1-8.1              scales_1.1.1              memoise_1.1.0            
 [94] magrittr_2.0.1            plyr_1.8.6                hexbin_1.28.1            
 [97] ica_1.0-2                 zlibbioc_1.34.0           compiler_4.0.3           
[100] RColorBrewer_1.1-2        pcaMethods_1.80.0         fitdistrplus_1.1-1       
[103] cli_2.1.0                 XVector_0.28.0            listenv_0.8.0            
[106] pbapply_1.4-3             ps_1.4.0                  htmlTable_2.1.0          
[109] ggplot.multistats_1.0.0   MASS_7.3-53               mgcv_1.8-33              
[112] tidyselect_1.1.0          stringi_1.5.3             forcats_0.5.0            
[115] yaml_2.2.1                latticeExtra_0.6-29       ggrepel_0.8.2            
[118] tools_4.0.3               future.apply_1.6.0        rio_0.5.16               
[121] rstudioapi_0.11           foreign_0.8-80            smoother_1.1             
[124] scatterplot3d_0.3-41      farver_2.0.3              Rtsne_0.15               
[127] digest_0.6.27             BiocManager_1.30.10       Rcpp_1.0.6               
[130] car_3.0-10                later_1.1.0.1             RcppAnnoy_0.0.16         
[133] httr_1.4.2                colorspace_1.4-1          fs_1.5.0                 
[136] tensor_1.5                ranger_0.12.1             reticulate_1.18          
[139] splines_4.0.3             fields_11.6               uwot_0.1.8               
[142] spatstat.utils_1.17-0     sp_1.4-4                  plotly_4.9.2.1           
[145] sessioninfo_1.1.1         xtable_1.8-4              jsonlite_1.7.1           
[148] spatstat_1.64-1           testthat_2.3.2            R6_2.5.0                 
[151] pillar_1.4.6              htmltools_0.5.1.1         mime_0.9                 
[154] glue_1.4.2                fastmap_1.0.1             VIM_6.1.0                
[157] class_7.3-17              codetools_0.2-16          maps_3.3.0               
[160] pkgbuild_1.1.0            mvtnorm_1.1-1             tibble_3.0.4             
[163] curl_4.3                  leiden_0.3.3              zip_2.1.1                
[166] openxlsx_4.2.2            limma_3.44.3              rmarkdown_2.5            
[169] desc_1.2.0                munsell_0.5.0             e1071_1.7-4              
[172] GenomeInfoDbData_1.2.3    haven_2.3.1               gtable_0.3.0             

Assigning identities to mutants

merge.anchors <- FindTransferAnchors(reference = tenx.justwt.integrated, query = GCSKO_mutants, 
    dims = 1:30)

Performing PCA on the provided reference using 2000 features as input.
Projecting PCA
Finding neighborhoods
Finding anchors
    Found 920 anchors
Filtering anchors
    Retained 610 anchors

predictions <- TransferData(anchorset = merge.anchors, refdata = tenx.justwt.integrated$monocle_sex, 
    dims = 1:30)

Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labels

#pancreas.query <- AddMetaData(pancreas.query, metadata = predictions)

table(df$predicted.id, df$identity_name_updated)

               
                fd1 fd2 fd3 fd4 gd1 md1 md2 md3 md4 md5 wild-type
  Asexual_Early   7  71   7  11  19   7  13   4  66  14        77
  Asexual_Late   60  52  94  14 148 121  43  41  43 187       194
  Female         21  62  19  23   6 127 205  15  25  88       197
  Male           65  55  14  45  60   0   0  17 112  47       221

table(df$predicted.id, df$fluoresence_sorted_on)

               
                GFP Hoechst mCherry
  Asexual_Early 178      69      49
  Asexual_Late  425     459     113
  Female        152     342     294
  Male          485      49     102

table(df_820$predicted.id, df_820$fluoresence_sorted_on)

               
                GFP Hoechst mCherry
  Asexual_Early   0      69       1
  Asexual_Late   33     459      16
  Female          2     342     240
  Male          182      49       0

Calculate sex ratios

## use designations above for sexes
male_cells <- rownames(mutant_seurat@meta.data[mutant_seurat@meta.data$predicted.id == "Male", ])
female_cells <- rownames(mutant_seurat@meta.data[mutant_seurat@meta.data$predicted.id == "Female", ])
ss2_mutants_final_male <- subset(mutant_seurat, cells = male_cells)
ss2_mutants_final_female <- subset(mutant_seurat, cells = female_cells)

## inspect
ss2_mutants_final_male

An object of class Seurat 
5018 features across 636 samples within 1 assay 
Active assay: RNA (5018 features, 0 variable features)

ss2_mutants_final_female

An object of class Seurat 
5018 features across 788 samples within 1 assay 
Active assay: RNA (5018 features, 0 variable features)

## calculate sex ratios
##subset out H, sorted cells:
df_male <- ss2_mutants_final_male@meta.data[ss2_mutants_final_male@meta.data$exclude_for_sex_ratio == FALSE,]

dim(df_male)

[1] 454 124

df_female <- ss2_mutants_final_female@meta.data[ss2_mutants_final_female@meta.data$exclude_for_sex_ratio == FALSE,]

dim(df_female)

[1] 546 124

## make dataframe
df_sex_ratio <- merge(
  as.data.frame(table(df_male$sub_name_updated)), 
  as.data.frame(table(df_female$sub_name_updated)), 
  by = "Var1", all=TRUE)

# or use identity_updated

## add names
names(df_sex_ratio) <- c("genotype", "male", "female")

## change the NAs to 0
df_sex_ratio[is.na(df_sex_ratio)] <- 0

## collapse 820 wild-types together
combined_m <- df_sex_ratio[df_sex_ratio$genotype == "WT-820_3_5", ]$male + df_sex_ratio[df_sex_ratio$genotype == "WT-820", ]$male
combined_f <- df_sex_ratio[df_sex_ratio$genotype == "WT-820_3_5", ]$female + df_sex_ratio[df_sex_ratio$genotype == "WT-820", ]$female
df_sex_ratio <- rbind(df_sex_ratio, c("WT-820-combined", combined_m, combined_f))

invalid factor level, NA generated

# remove old rows
df_sex_ratio <- df_sex_ratio[-which(df_sex_ratio$genotype == "WT-820_3_5" | df_sex_ratio$genotype == "WT-820"), ]
# need to make numeric again
df_sex_ratio$male <- as.numeric(df_sex_ratio$male)
df_sex_ratio$female <- as.numeric(df_sex_ratio$female)
df_sex_ratio$genotype <- as.character(df_sex_ratio$genotype)
# add name for WT combined
df_sex_ratio$genotype[17] <- "WT-820-combined"

## calculate sex ratio
df_sex_ratio$sex_ratio <- (df_sex_ratio$male + 0.1)/(df_sex_ratio$female + 0.1)

## log sex ratio
df_sex_ratio$sex_ratio_log <- log10(df_sex_ratio$sex_ratio)

##view
df_sex_ratio

## remove WT-2 because it is really inappropriate to have a sex ratio for this:
df_sex_ratio <- df_sex_ratio[-which(df_sex_ratio$genotype == "WT-md3"),]

plot

library(latex2exp) # so you can plot the fraction, it's on CRAN `install.packages("latex2exp")`

## make extra column for plotting aesthetics:
df_sex_ratio$above <- df_sex_ratio$sex_ratio_log > 0

## make extra column with ratio in it:
df_sex_ratio$genotype_with_n <- paste0(df_sex_ratio$genotype, " (", df_sex_ratio$male, "/", df_sex_ratio$female, ")")

## reorder genotype so it is in the correct order for plotting
df_sex_ratio$genotype_with_n <- factor(df_sex_ratio$genotype_with_n, levels = df_sex_ratio$genotype_with_n[order(df_sex_ratio$sex_ratio_log)])

## plot
sex_ratio_plot <- ggplot(df_sex_ratio, aes(sex_ratio_log, genotype_with_n, color = above)) +
      ## add the lines for the lollipop plot
      geom_segment(aes(x = 0, y = genotype_with_n, xend = sex_ratio_log, yend = genotype_with_n), color = "grey50") +
      ## add the points for the lollipop plot
      geom_point(aes(size = 4)) +
      ## add the wild-type rectangle
      annotate("rect", xmin= -0.23182478, xmax = 1.00105797, ymin=-Inf , ymax=Inf, alpha=0.4, color=NA,linetype = 2, fill="#999999") +
      ## make prettier
      theme_classic() +
      theme(legend.position = "none", text=element_text(size=16, family="Arial")) + 
      ## change axis labels
      labs(y = expression(paste("Genotype", group("(", frac(paste("n male"), "n female"), ")")))) +
      xlab(expression(paste("Sex Ratio (", log[10], 
                               group("(",
                                      frac(paste("n male + 0.1"), 
                                           paste("n female + 0.1")),
                                   ")"), ")" ))) +
      ## change colours of lollipops
      scale_colour_manual(values = c("#a52b1e", "#016c00")) +
      ## annotate phenotypes
      geom_hline(aes(yintercept = 5.5)) #+
      #geom_hline(aes(yintercept = 14.5))

print(sex_ratio_plot)

#paste("Sex Ratio", "\n", "log10((n male + 0.1)/(n female + 0.1))")
#theme(legend.position = "none", text=element_text(size=16, family="Arial"))

## fraction titles: https://groups.google.com/forum/#!topic/ggplot2/bgNRnZ82hJY 
## https://uc-r.github.io/lollipop

ggsave(filename = "~/images_to_export/sex_ratio_plot.png", device = "png", width = 7, height = 7, units = "in")

Error in grDevices::dev.off() : 
  QuartzBitmap_Output - unable to open file '/Users/ar19/images_to_export/sex_ratio_plot.png'

SCMAP

Build the index

## load in mca data
counts <- read.csv("../scmap/allpb10x_counts.csv", row.names = 1)
pheno <- read.csv("../scmap/allpb10x_pheno.csv")
ggplot(pheno, aes(x=PC2_3d, y = PC3_3d,colour=absclust3)) + geom_point()

###Making an ortholog reference index

## load required libraries
library(scmap) #https://bioconductor.org/packages/release/bioc/html/scmap.html 
library(SingleCellExperiment) #

#prep the SCE, if was originally a Suerat object need the dfs to be regular matrices
#pb_filtered_sce_orth <- pb_filtered_sce_orth[, colData(pb_filtered_sce_orth)$absclust3 != "8"]
#sce <- pb_filtered_sce_orth
#pca <- plotPCA(sce)
#pcs <- pca$data
#table(rownames(pcs)==colnames(sce))
#colData(sce) <- cbind(colData(sce), pcs)
#rowData(sce)$feature_symbol <- rowData(sce)$gene

## extract counts
cells_tenx <- rownames(tenx.justwt.integrated@meta.data[which(tenx.justwt.integrated@meta.data$experiment == "tenx_5k"), ])
tenx.justwt.integrated.10k <- subset(tenx.justwt.integrated, cells = cells_tenx)
counts = as.matrix(GetAssayData(tenx.justwt.integrated.10k, slot = "counts", assay = "RNA"))
pheno = as.data.frame(tenx.justwt.integrated.10k@meta.data)

sce <- SingleCellExperiment(list(counts=counts),
    colData=DataFrame(label=pheno),
    rowData=DataFrame(feature_symbol=rownames(counts)))
sce

class: SingleCellExperiment 
dim: 5098 6191 
metadata(0):
assays(1): counts
rownames(5098): PBANKA-0000101 PBANKA-0000301 ... PBANKA-MIT0360 PBANKA-MIT0370
rowData names(1): feature_symbol
colnames(6191): AAACCTGGTAAGGGCT AAACCTGGTGCACTTA ... TTGTAGGCACCCATTC TTTATGCTCCCTAATT
colData names(125): label.orig.ident label.nCount_RNA ... label.monocle_sex label.pt_id_cols
reducedDimNames(0):
altExpNames(0):

counts_1 <- assay(sce, "counts")
libsizes <- colSums(counts_1)
size.factors <- libsizes/mean(libsizes)
logcounts(sce) <- log2(t(t(counts_1)/size.factors) + 1)
#counts(sce) <- as.matrix(counts(sce))
#logcounts(sce) <- as.matrix(logcounts(sce))
# remove features with duplicated names
sce <- sce[!duplicated(rownames(sce)), ]

#build scmap-cell reference index, save this rds
sce <- selectFeatures(sce, suppress_plot = FALSE, n_features = 1000)

table(rowData(sce)$scmap_features)

FALSE  TRUE 
 4098  1000 

set.seed(1)
sce <- indexCell(sce, M = 50, k = 80)
names(metadata(sce)$scmap_cell_index)

[1] "subcentroids" "subclusters" 

length(metadata(sce)$scmap_cell_index$subcentroids)

[1] 50

dim(metadata(sce)$scmap_cell_index$subcentroids[[1]])

[1] 20 80

metadata(sce)$scmap_cell_index$subcentroids[[1]][,1:5]

                         1          2           3           4           5
PBANKA-0000600 0.000000000 0.00000000 0.017319817 0.012189578 0.003588831
PBANKA-0100021 0.000000000 0.00000000 0.009273378 0.012008644 0.007037379
PBANKA-0100061 0.000000000 0.00000000 0.000000000 0.000000000 0.003188301
PBANKA-0100400 0.000000000 0.00000000 0.000000000 0.000000000 0.003529208
PBANKA-0100700 0.009256190 0.03564637 0.004582741 0.711759454 0.512994920
PBANKA-0100900 0.000000000 0.12850312 0.000000000 0.029992203 0.116275683
PBANKA-0102100 0.009788597 0.04550907 0.046740159 0.055214920 0.026074044
PBANKA-0102200 0.125516716 0.16411025 0.973661503 0.648046746 0.094371601
PBANKA-0102700 0.000000000 0.01078990 0.000000000 0.011205161 0.006195530
PBANKA-0104800 0.067747905 0.08217666 0.000000000 0.017921412 0.045057969
PBANKA-0105100 0.016050292 0.01032653 0.014396913 0.000000000 0.000000000
PBANKA-0105200 0.083473250 0.64525750 0.000000000 0.244919487 0.084043062
PBANKA-0105300 0.645891226 0.04307012 0.006987481 0.047515307 0.033900224
PBANKA-0105600 0.022291736 0.09764748 0.004582741 0.030591318 0.436290871
PBANKA-0106300 0.026879908 0.01959103 0.012314088 0.000000000 0.019707527
PBANKA-0106500 0.128486633 0.03548444 0.017194136 0.008153711 0.017918030
PBANKA-0107300 0.730339642 0.03666156 0.006987481 0.018961331 0.576932803
PBANKA-0108200 0.050668751 0.01506403 0.140644213 0.003876659 0.032560201
PBANKA-0109100 0.013577868 0.71758975 0.169816579 0.070737617 0.422366474
PBANKA-0109900 0.034604488 0.01845691 0.000000000 0.011054729 0.000000000

dim(metadata(sce)$scmap_cell_index$subclusters)

[1]   50 6191

#saveRDS(pb_filtered_sce_orth, file="pb_filtered_sce_orthindex_20181109.rds")

Map to the index

LIVE

#rowData(pb_filtered_sce_orth)$feature_symbol <- rowData(pb_filtered_sce_orth)$orth_name
#rownames(pb_filtered_sce_orth) <- rowData(pb_filtered_sce_orth)$orth_name

#prep the SCE, if was originally a Suerat object need the dfs to be regular matrices
#pb_filtered_sce_orth <- pb_filtered_sce_orth[, colData(pb_filtered_sce_orth)$absclust3 != "8"]
#sce <- pb_filtered_sce_orth
#pca <- plotPCA(sce)
#pcs <- pca$data
#table(rownames(pcs)==colnames(sce))
#colData(sce) <- cbind(colData(sce), pcs)

pf_field_counts = as.matrix(GetAssayData(mutant_seurat, slot = "counts", assay = "RNA"))
pf_field_pheno = as.data.frame(mutant_seurat@meta.data)

#rowData(sce)$feature_symbol <- rowData(sce)$gene
pf_live_field.sce.orth <- SingleCellExperiment(list(counts=pf_field_counts),
    colData=DataFrame(label=pf_field_pheno),
    rowData=DataFrame(feature_symbol=rownames(pf_field_counts)))
pf_live_field.sce.orth

class: SingleCellExperiment 
dim: 5018 2717 
metadata(0):
assays(1): counts
rownames(5018): PBANKA-0000101 PBANKA-0000301 ... PBANKA-1466201 PBANKA-1466241
rowData names(1): feature_symbol
colnames(2717): SC26779_5_100 SC26779_5_101 ... SC25027_8_99 SC25027_8_9
colData names(124): label.orig.ident label.nCount_RNA ... label.prediction.score.Female label.prediction.score.max
reducedDimNames(0):
altExpNames(0):

counts_1 <- assay(pf_live_field.sce.orth, "counts")
libsizes <- colSums(counts_1)
size.factors <- libsizes/mean(libsizes)
logcounts(pf_live_field.sce.orth) <- log2(t(t(counts_1)/size.factors) + 1)


#Project query data set onto cell index
scmapCell_results <- scmapCell(
  pf_live_field.sce.orth, 
  list(yan = metadata(sce)$scmap_cell_index
  )
)

##Look into the results
# For each dataset there are two matricies. cells matrix contains the top 10 (scmap default) cell IDs of the cells of the reference dataset that a given cell of the projection dataset is closest to:
#   
#   Give assignments in two ways:
#   1. Take the top cell assignment abs clust, if cosine similarity is less than 0.4 (or adjust if needed) mark as unassigned
# 2. For the top 3 nearest neighbors, get a mean of the PCA coordinates and snap to the nearest cell of those coordinates. If any of the top three cells are sim below 0.4 then mark as unassigned.


##Top cell assignment method
scmapCell_results$yan$cells[, 1:3]

      SC26779_5_100 SC26779_5_101 SC26779_5_102
 [1,]          5492          5647          6183
 [2,]          5648          5648          6149
 [3,]          5638          5617          6139
 [4,]          5587          5551          6171
 [5,]          5542          5492          6174
 [6,]          5592          5585          6124
 [7,]          5577          5542          6135
 [8,]          5691          5486          6130
 [9,]          5551          5505          6162
[10,]          5647          5638          6134

getcells <- scmapCell_results$yan$cells[1, ]
cdsce <- colData(sce)[getcells, ]
topsim <- scmapCell_results$yan$similarities[1, ]

pf_live_field.sce.orth$topcell <- cdsce$label.sample_id
pf_live_field.sce.orth$topcell_ac <- cdsce$label.monocle_sex
pf_live_field.sce.orth$indexPC1 <- cdsce$label.PC1
pf_live_field.sce.orth$indexPC2 <- cdsce$label.PC2
#pf_live_field.sce.orth$pbpt <- cdsce$pseudotime
#pf_live_field.sce.orth$pbbulk <- cdsce$bulk
pf_live_field.sce.orth$topcell_sp <- pf_live_field.sce.orth$topcell_ac
pf_live_field.sce.orth$topsim <- topsim
pf_live_field.sce.orth$topcell_sp[pf_live_field.sce.orth$topsim < 0.5] <- "unassigned"
table(pf_live_field.sce.orth$topcell_sp)

Asexual_Early  Asexual_Late   Bipotential        Female          Male    unassigned 
          263           930             4           781           602           137 

table(pf_live_field.sce.orth$topcell_sp, pf_live_field.sce.orth$label.identity_name_updated)

               
                fd1 fd2 fd3 fd4 gd1 md1 md2 md3 md4 md5 wild-type
  Asexual_Early   7  56   8   9  17   9  12   6  57  13        69
  Asexual_Late   46  51  92  14 140 118  42  37  40 166       184
  Bipotential     0   0   0   0   0   1   0   0   0   1         2
  Female         18  63  19  23   5 125 205  15  24  87       197
  Male           64  55  14  45  48   0   0  16 106  36       218
  unassigned     18  15   1   2  23   2   2   3  19  33        19

table(tenx.justwt.integrated@meta.data$fluoresence_sorted_on)